System, method and apparatus for organizing groups of self-configurable mobile robotic agents in a multi-robotic system

ABSTRACT

A system of self-organizing mobile robotic agents (MRAs) in a multi-robotic system (MRS) is disclosed. MRAs cooperate, learn and interact with the environment. The system uses various AI technologies including genetic algorithms, genetic programming and evolving artificial neural networks to develop emergent dynamic behaviors. The collective behaviors of autonomous intelligent robotic agents are applied to numerous applications. The system uses hybrid control architectures. The system also develops dynamic coalitions of groups of autonomous MRAs for formation and reformation in order to perform complex tasks.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application claims the benefit of priority under 35 U.S.C.§119 from U.S. Provisional Patent Application Ser. Nos. 60/404,945 and60/404,946, filed on Aug. 21, 2002, the disclosures of which are herebyincorporated by reference in their entirety for all purposes.

BACKGROUND OF THE INVENTION

There are several categories of prior art patents that apply to thepresent invention. These patents involve mainly mobile robots and groupsof mobile robots.

Matsuda (robot system and control device), U.S. Pat. No. 5,825,981;Peless et al. (method for operating a robot), U.S. patent applicationpublication number # 20010047231; and Nourbakhsh et al. (sociallyinteractive autonomous robot), U.S. patent application publicationnumber # 20020013641, mobile robots are used automatically, or withmanual intervention to perform tasks such as multifunctionalmanufacturing, cleaning, mowing, snow blowing or interacting withhumans. These pedestrian approaches to robotic control fit into the mainparadigm of robotic applications.

Kawakami (mobile robot control system), U.S. Pat. No. 5,652,489; Asamaet al., (mobile robot sensor system), U.S. Pat. No. 5,819,008; andWallach et al. (autonomous multi-platform robot system), U.S. Pat. No.6,374,155 involve multiple mobile robots. These patents involve usingsensors for navigation and obstacle avoidance. In addition, one mobilerobot can transmit information to another mobile robot for some effect.These inventions offer only rudimentary connections between robots andlack advanced system functions.

Most of the research history involving the technologies of the presentsystem—including (1) intelligent agents and self-organizing systems, (2)AI and D-AI in coordinated systems, (3) negotiation and problem solvingand (4) cooperating agents and aggregation—are represented in theacademic literature, described below.

The development of complexity theory is fairly recent. Theorists fromeconomics and biology advanced the view in the 1980s that systems areself-organizing and adaptive of their environments. In particular,biologists have studied ant and insect social organization and haveobserved the complex adaptive behaviors of these societies.

Researchers at the Sante Fe Institute (SFI) have developed complexitytheory by looking at the fields of biology, economics, mathematics,epistemology and computer science. One of the aims of the SFI is todevelop a complex self-organizing computer model representing artificialautonomous agents that emulate the biological functions of complexinsect social behavior.

SFI theorists have developed the swarm intelligence model of artificialcomputer societies primarily for simulating economic systems. The swarmintelligence model, by emulating biological system operation, uses ideasof emergent behavior to describe the complex social interactions ofrelatively simple insects according to straightforward decentralizedrules governing group activity.

The challenge for computer scientists lies in how to develop a system ofself-organized autonomous robotic agents. The development of societiesof behavior-based robotics that fuse elements of system control withelements of decentralized local control is one of the most difficultchallenges in computer science and robotics. A key part of this problemlies in how to configure AI systems for problem solving in a MRS forcollective behavior. In short, how can we design an intelligent MRS foroptimal adaptation to dynamic environments?

The computer science fields of robotics and AI have evolved in the pastdecade in such a way that a convergence of technologies allows anexplosion in research in collective robotics and in intelligent systemsin order to achieve the goals of developing an intelligent MRS for groupbehavior. For example, rapid advances in computation resources,communications and networking allow the combination of integratedtechnologies necessary for a development of a sophisticated MRS. Inaddition, in the area of AI research, several trends have emerged,including GA, GP, A-NN and distributed AI, that allow computer systemsto not only learn but achieve some degree of autonomy.

In the early 90s, Brooks developed a decentralized modular approach torobotics at MIT's Media Lab. Revolutionary at the time because itspurned conventional wisdom of highly computation-intensive deliberativerobotic control approaches, his modular approach used less than threepercent of traditional computer approaches. This leap in efficiency wasachieved by separating the subsystems for automatic reactive control (hecalled it subsumption) rather than deliberative top-down robot systemcontrol. The mobility, navigation and pick-up functions of the robotcould be separated for increased efficiency.

By exploiting this research stream, Arkin (1998) developed abehavior-based model of robotics. In this model, Arkin describesbehavior-based robotic architectures as well as experiments in the fieldwith sophisticated hybrid robotic architectures. An example of thishybrid approach is NASA's Atlantis system (1991) that synthesizesdeliberative planning with group behavior. The aim of these models is todevelop autonomous robots that are adaptive to their environment. Thedevelopment of robotic teams with social behavior is one of the mostdifficult challenges, according to Arkin's pioneer study.

Bonabeau et al. (1999), an SFI fellow, develops a research stream thatconnects the study of ant and insect behavior in complex biologicalsocial systems with the development of complex artificial roboticsocieties. In their vision of swarm intelligence, they use key notionsof system self-organization, reactive behavior and environmentaladaptation to point to a model for artificial robotic systems that mightemulate biological systems.

In 2001, Kennedy and Eberhart focused on the social and theoreticalaspects of swarm intelligence. Their examination of group behaviordevelops a computer model of adaptive self-organized systems, similar toeconomic “particle” simulations by the SFI, by emulating the socialbehavior of biological systems. In order to develop an artificial swarmsystem, the authors look to complex pattern emergence, which has alineage from Von Neumann to Burks to Wolfram. In this research stream,cellular automata are used to simulate a complex but stableself-organizing system. Though the authors refer to research experimentswith robot societies, their focus remains on computer and theoreticalmodels of complex social behavior involving autonomous entities.

Another important research stream involves the application of AI tonetworks. The emergence of the Internet has presented novel ways toconduct commerce automatically with autonomous software agents in a MAS.Originally developed by Smith, the contract-net protocol established anearly model for distributed problem solving. As the Internet evolvedrapidly, new computational systems emerged to emulate commercialsystems. Solomon has developed demand-initiated self-organizingcommercial systems for both intermediated and dis-intermediatedtransactions that employ novel multivariate and multilateral negotiationmodels.

One niche of the automated commerce system lies in the aggregation ofautonomous agents. Precisely how to combine pools of autonomous agentsfor wholesale discounts presents an opportunity to remove a layer ofdistribution from commercial systems. This research stream is importantbecause it provides clues as to how to develop coalitions of roboticagents for common purposes.

MRS models have been developed. The Nerd Herd is an example of an MRSusing rule-based social behaviors for subsumption based foragingpopularized by Brooks. Second, the Alliance architecture developed amodular approach to robot team behavior that includes inter-robotcommunication. Such communication allows for emergent cooperation. Anadditional version of Alliance (L-Alliance) accommodates the learningaspect of robotic agents in order to achieve a form of adaptation.

Arkin developed a “multiagent schema-based robotic architecture” inwhich team cooperation was modeled using a behavior-based approachwithout explicit inter-robot communication.

Dias and Stentz provide a market-based model for multiroboticcoordination in which individual robots in a distributed environmentnegotiate with each other in order to agree upon a course of action.Such a model applies the contract-net protocol used with software agentsin a distributed network to the robotics context for operation of groupsof autonomous robots in dynamic environments.

Finally, Solomon developed a hybrid MRS model with military andindustrial applications in which a hierarchical leader-follower approachis implemented in a hybrid central-control and behavior-based controlarchitecture.

Most MRSs possess several common traits, including mobility,intelligence, communications, group behavior and specific functionality.

One critical aspect of robotic group behavior lies less in the value ofintelligence that in the importance of methods of aggregation. It is akey challenge of robotic systems of determine ways for robotic agents tosynchronize, cooperate and collaborate and, in sum, to work together asa team. The emergence of dynamic coalitions of robotic groups is one ofthe most interesting and important areas of robotic research.

The effort to achieve the development of complex MRSs that may emulate,and even transcend, emergent natural self-organizing processes, hasbecome primarily a computation challenge that involves the need tocreate sophisticated AI architectures. AI systems have themselvesemulated biological systems, with the advent, from Holland and Koza tothe present, of genetic algorithms, genetic programming and evolutionarycomputation methods in order to solve complex problems. A relatedresearch stream involves A-NN, which has utilized GA in order toestablish weight values of neural nodes. One main aim of the neuralnetworks is to develop self-configuring and self-organizing learningsystems for complex problem solving. This is useful in real timecollective robotics situations in which rapid adaptation to a changingenvironment is necessary.

The development of hybrid AI technologies that synthesize variousmethods for specified problem solving would provide a robust andsuccessful option in the computer scientist's arsenal of weapons thatmay be useful for the development of sophisticated MRS architectures.

BRIEF SUMMARY OF THE INVENTION

The present inventions involve multi robotic systems, multi agentsystems, collective robotics, artificial group behaviors, aggregation ofrobotic agents, coalition formation, dynamic coalitions,self-organization of robotic agents, emergent behavior of intelligentagents, cooperation of intelligent agents, multi agent learning, problemsolving between conflicting intelligent agents, artificial intelligence,artificial neural networks and multi robotic operating systems.

Multi-robotic systems are complex networks that facilitate theinteraction between autonomous robotic agents according to specificrules of behavior in order to perform a specific function or combinationof functions. The present invention describes a system for multiplemobile robotic behavior by applying the logic of advanced computerscience, in particular artificial intelligence (AI), with advancedrobotic electronics and mechanics. The focus here is on artificialrobotic collectives. So far very little research has been developed onthe group behavior aspects of robotic societies as they plan, and thenachieve, a coordinated goal.

There are several layers of any such collective robotic system,including (1) the computation, electrical and mechanical hardware ofeach autonomous robot unit, (2) a hardware network layer that links theindividual robots together with wireless communications, (3) ametacomputing layer (that performs complex memory, database andcomputation analysis functions) in a node to node distributed computingmodel, (4) an omni-nodal artificial neural network (A-NN) layer fordistributed AI, (5) an evolutionary A-NN layer—driven by geneticalgorithms and genetic programming—for adaptive group learning in orderto develop real-time cellular automata (CA) based simulations to seekoptimal system solutions, (6) an OS layer and (7) a layer for specificfunctional applications.

The present invention describes a sophisticated MRS that is dynamic,interactive and evolving, adaptive to its environment and capable ofexhibiting emergent behavior. The system is designed as a hybrid ofbehavior-based and central planning control processes in a distributednetwork environment. By decentralizing numerous functions in adistributed architecture model, groups of autonomous robotic agents canlearn together, make group decisions together (cooperatively andcompetitively), negotiate and solve problems together, congregatetogether in various sub-sets and re-configure in non-overlappingsub-groups. Using these unique approaches, autonomous robotic agents canform and reform into various configurations of groups in aself-organized way interacting with each other and with the environmentin order to achieve pre-programmed, or evolved, goal parameters.

Artificial intelligence (AI) is used in a number of MRS processes,including individual robot learning and decision making using geneticalgorithms (GAs), genetic programming (GP) and other evolutionarycomputation (EC) approaches as well as group robotic agents that usesA-NN and hybrid evolutionary A-NN approaches (including GA, GP, FL,etc.) that provide tools for adaptive collective learning and decisionmaking. The use of both individual agent and group learning tools areimportant because though the collective resources are far greater, whenthe system defaults to behavior-based biases, for instance, insituations with diminished computation resources, it is necessary forthe individual robotic agents to have the tools to maintain autonomy. Bybuilding on the lower layers of behaviors of reactive approaches, a morecomplex MRS can evolve beyond ant society emulation.

In practical terms, MRS operation in unknown environments presentsnumerous challenges and problems to solve. In the absence of acentralized “mission control” station to solve all the problems arobotic system may encounter, there must be a number of fall back systemmodes in order for the mission to be successful, which leads to ahierarchy of system structures. These system modes are dependent oncomputation resources, communications resources, levels of robotic agentautonomy, levels of learning and levels of group behavior.

In earlier multirobotic systems, a relatively simple architecture wouldconsist of a leader robot with various followers in a hierarchy. Theleader possesses increased autonomy and orders the followers(super-drones). In this model, pre-selected squadrons are formed, thecontrol for which can be manually intervened by human interactionprocesses such as a video feed for mission objective alteration.Reprogrammable orders and priorities can be uploaded at any time.

In one embodiment, supplementary external computation resources can bekept outside of the MRS and fed in as needed by satellite.Alternatively, though computation is performed externally to the MRS,analytical results can be used to control the system. In addition,reporting on agent behavior can be provided to an off-site blackboard soas to unify control at a central command center.

As the system and its agents gain autonomy, increased capacities arebrought in, such as computation power, communication bandwidth and AIcapabilities. Still, only reactive behavior-based autonomous roboticagent interaction approaches would yield a relatively simple system thatappears to generate group behavior but merely mimics collective actionsbecause of the outcome of interactions between autonomous agents. Thesystem in this mode is merely semi-autonomous, which reflects itsresource limits.

As the MRS system is linked together in a distributed network ofautonomous robotic agents that employ powerful computation resources andAI processes, the system can automatically “think” like a group andconstantly reconfigure to the best available situation while interactingwith and adapting to its environment.

It is therefore valuable that the system, though using a hybridarchitecture, employ a number of distinct embodiments that accommodatechanges and that automatically default to the most complex taskachievable.

Hybrid MRS Architecture with Distributed Resource Management and CommandStructure

A pure behavior-based reactive MRS architecture has advantages of localcontrol and emergent behavior but disadvantages of the inability tocontrol large groups in complex adaptive environments. On the otherhand, a central deliberative MRS control architecture has the ability todevelop large self-organizing interactive systems and sub-systems buthas the limits of being cumbersome and dependent on substantialcomputation resources. What is needed in order to build and operate acomplex and high performance MRS is a hybrid architecture. In effect,the MRS architecture is a complex, continuously reconfiguring, operatingsystem that links together robotic agents with computation,communications and software subsystems. Such a system must be modular(so that upgrades in a subsystem can be seamlessly performed), scalable(so that nodes can be added or removed) and reconfigurable. The systemuses mobile software program code that provides inputs and outputs torobot machine agents. The “Harness” dynamic reconfigurable metacomputingmodel is a pioneer for this mobile self-organizing MRS hybrid approachbecause it continuously seeks to re-route the system to the optimalcomputation and communication pathways.

On a lower level, each robotic machine unit has sensors, actuators,microprocessors, communication receivers and transmitters, power supply,a specific functionality and (system and applications) software.However, when they are linked together, the opportunity exists for theMRS specific mobile robotic unit sensors to be organized into a networkfor collective data acquisition. The group's collective computationresources can analyze the sensor data. In addition, the group of mobilerobotic agents can use complex AI induced learning processes to makegroup decisions, even in the face of noisy, error-prone and conflictingdata streams. By maximizing the efficiency of the available group MRSresources, intelligent group behavior can emerge.

The aggregation of MRAs into subgroups can occur, further reconfiguringin complex ways in dynamic and changing environments. By learning andworking as a group, specific autonomous agents are altruistic and may besacrificed for the greater whole if it is necessary in a specificcritical operation. Further, specific sub-groups may conflict and splitthe herd in order to achieve different objectives. The convergence oftechnologies that allows teams of autonomous MRAs to worktogether—computation resources and reconfiguration, communicationsbandwidth capacity and complex system software—make possible arevolution that emulates how groups of humans think and behave.

In order to make this technology convergence operability possible, it isnecessary to develop a distinctive hybrid MRS architecture for adistributed self-organizing system. Such a hybrid system accommodateslower-level bottom-up reactive modular behavior-based approaches as wellas the use of sophisticated hybrid AI resources (D-AI, A-NN, GA, GP,etc.) that work in a distributed system for group learning processesapplied to complex decision processes, optimal simulation and collectiverobotics actions in dynamic environments. Such a hybrid model allows foradaptation in uncertain environments while also being able to carry outinitial, and evolving, program objectives.

If one compares how animals work in groups we see a resemblance to oursystem. Though specific animals have sensory data, memory, navigation,data analysis, decision-making and action sub-system abilities, as agroup collectives of animals can achieve marked performance improvementsbecause they have more data and analytical capacities and theintegration of successful actions that increase the probabilities ofwinning at foraging for food or defending against attacks. Why, then,cannot an MRS be developed that emulates, and even transcends, theperformance of the animal (and insect) group model?

Historically, one of the main problems in building such an intelligentMRS of autonomous self-organizing MRAs has been computer resourceconstraints. There is the limit, not only of computer capacity, but alsotime, constraints. A huge amount of data must be processed in a shorttime while the MRS is operational; in essence, the system must computeon the fly as it gathers and understands data and decides what to do andthen how to act as a group. There are practical solutions to theseresource constraint problems. First, the application of Grid computingmodels provides an appropriate distributed model for maximizingcomputation capacity by sharing resources among MRAs in real-time. Thismodel can be scalable so that new MRAs can be added as needed even ifothers are subtracted as the mission requires. In fact, each agent canbe re-tooled and upgraded in each reuse of the modular system.

Second, multiple communications topologies can be used to re-route datastreams to the most efficient use within the distributed system,including using advanced caching techniques for optimal collectiveeffect. Finally, AI software can be employed for learning, negotiation,decision and simulation of complex collective behaviors. The system thendetermines, while it is mobile, what to do and then acts as a team tocooperatively achieve the objective. By overcoming the resourceconstraints with collective action, an intelligent MRS emerges.

The present system is therefore far more advanced than previous MASapproaches that seek to emulate the behavior of groups of simple insectsbecause our system is endowed, not only with autonomous agentintelligence, but with collective group intelligence that transcendssimple group behaviors. It is clear, then, that in order to develop suchan advanced MRS, hybrid or meta-architectures must be employed thatcombine both local and global aspects.

Towards a Hybrid MRS AI Model: Distributed Problem Solving, IntegratedGroup Learning, Decision Processes and Dynamic Optimization Simulationswith Cellular Automata

AI has emerged in the past generation as a valuable tool for solvingcomplex problems. Genetic algorithms, developed by Holland and others,are a problem solving method to evolve, through reproduction, crossoverand mutation techniques, algorithms. Genetic programming and otherevolutionary computation approaches seek to solve different domains ofproblems. These complex strategies seek to emulate natural evolutionprocesses so as to find the fittest, most efficient or optimalsolutions.

The development of artificial neural networks (A-NN) was initiallyintended to emulate brain function. Referred to as connectionism, A-NNuses GA and FL (soft computing) techniques to map out, train andreconfigure a network of nodes for solving problems. By using anadaptive network architecture topology, the A-NN system can optimizeadaptation to its environment. By training the network over distributedgroups of agent nodes, the A-NN can learn. Evolutionary A-NN (E-A-NN),or neurevolution, is useful for reinforcement learning. A-NN's work byusing genetic algorithms to adapt input features, learning rules andconnection weights. One of the most effective applications for A-NN isnonlinear statistical models such as pattern recognition. A-NN's learnby altering synaptic weights; synaptic weight variables change by usingfuzzy logic techniques to assess probabilities and thresholds. Bayesiannetworks use hypotheses as intermediaries between data and predictionsto make probability-based estimates of solutions. Hopfield networks areused to remember an earlier network configuration and to revert to anold network when noisy data limits continuing network development.

The present invention uses a hybrid approach to AI that combines GA andGP with A-NN and D-AI architectures. The combination of evolutionarycomputation approaches with distributed neurocomputing models produces asystem that constantly rewires itself as the system is reconfigured.This approach is necessary because finite computation resources need tobe maximized even while the distributed mobile MRS changes. Not only isthis scheme scalable but increased computation capacity can be providedon demand if needed by specific under used MRAs. Such a hybrid AIarchitecture is best suited for learning by groups in a distributednetwork as well as for optimal adaptation to dynamic environments.

Hybrid AI approaches can be useful when solving complex problems. Twomain problem solving models involve either cooperative (altruistic) orconflict (self-interested) oriented agent behavior. One maincomputational challenge that involves MRS is the distributed problemsolving that requires negotiation among conflicting autonomous agents.

Conflicting MRAs use AI approaches to negotiate a settlement so as tosolve complex multilateral disagreements. One way for groups to solveproblems in a conflicted MRS is by finding proper matches for sharedcommon interests, thereby focusing on the limited remaining variablesand disagreements. This pruning process can settle an issue either bypre-determined (or changing) rules or by a vote between involved agents.In this way teams of MRAs can compete for effective solutions. Anothermethod to find solutions in conflicted MAS situations is to set up acompetition for the strongest strategies according to agreed upon rules.Finally, an agent can persuade other agents to its position.

All of these models involve inter-agent collaboration for complex groupproblem solving. The resolution of competing rival MRAs conflicts resultin agreement about an optimal solution. Through conflict andcompetition, not only is common ground sought, but a winning algorithmsolution is determined for complex problems. This problem solvingnegotiation approach is useful for organizing heterogeneous MRAs forcommon objectives.

How are negotiations between MRAs in a MRS processed? Autonomous roboticagents use complex decision processes that ultimately affect groupbehavior. Decisions can be made by either individual agents or by groupsof agents. Rules are used to prioritize specific possible choices overothers. Upon achievement of a specific threshold, a decision processyields a resulting choice of possible options. Once a threshold isachieved, a plan of action can be implemented.

Since it is important to configure group decision processes for MRSproblem-solving, a range of decision choice constraints present thelower and upper bounds of potential optimal solutions. Further, theseparameters are constantly shifting in dynamic environments. Hence,methods need to be devised to find the shortest path to perform specifictasks. One way to do this is to perform specific tasks. One way to dothis is to use statistical weighting to prioritize problems and solutionpossibilities. In the context of complex changing environments, an MRSmust simultaneously work on solving numerous constantly changingproblems. The Markov decision process makes decisions by prioritizingpossible choice as measured by evolving values criteria.

MRS action starts with a plan. By mapping the parameters of group actionplans, we can model the optimal configuration or allocate the mostefficient resources. Decision logic processes lead to identifying tradeoffs (parameters) between possible solutions that lead to an optimalproblem solving choice. MRAs use computation optimization techniques toselect optimal solutions to complex problems in uncertain environments.By mapping various scenarios, using AI and decision processes in adistributed network, MRAs select the best plan to achieve objectives.

MRSs use advanced hybrid AI methods in order to achieve optimal groupingpatterns of behavior. Unlike purely computational MASs, a MRS havephysical dimension and motion in space. These physical and geometricrealities about the practical operations of MRSs involve the need toorganize spatial interactions and movements. It is useful to model theseMRA movements before actually performing specific maneuvers primarilythrough the use of simulations.

Cellular automata (CA) models provide an important tool to simulate thechanging movements of MRAs in an MRS. By using AI approaches, each robotis represented as a cell in a larger system. Cells can interact withneighbor cells in the neighborhood of a CA system, with two dimensional,three dimensional or four dimensional models representing the change incellular states.

The results of combinatorial optimization approaches to seek the bestsolutions to solve problems can be represented by CA simulations and,thereby, tested, before actually implementing these decision choices. Bymodeling group behavior in real time, the MRS solves problems and canseek improved solutions that can capture subtle contingencies in complexoperational situations. MAS swarms are tested in particle simulationsusing CA models, but MRSs have not applied these important CA drivensimulations for real geometric behaviors. Therefore, the presentinvention uses simulations in a dynamic, rather than merely static, way,for real time testing. In the simulation, virtual robots are providedthe valuable advantage of trial and error of potentialities of activityso as to learn from complex contingencies, in order to optimize thechances for mission success.

Swarms, Flocks, MRS Aggregation and the Formation and Reconfiguration ofDynamic Coalitions

Nature provides analogies for computer scientists in the contexts of AIand group robotics. In the case of AI, GAs and GPs seek to emulatenatural selection by breeding the best fit problem-solving programsusing principles of sexual reproduction, pruning and random mutation. Inthe case of robotic group behavior, scientists have sought to emulateinsect (ant and bee) social behaviors in order to understand howcompolex patterns emerge from simple individuals. How can MRSs bedeveloped that have the self-organizing properties of insects? The twomain behaviors that have intrigued observers are foraging (food locationsearch and procurement) and swarming behaviors.

Scientists have discovered that ants use pheromones (chemicals that havean odor to attract others) to develop complex foraging behaviors. Bylaying down pheromones, which, though temporary, can be increasinglyintense if compounded, ants provide a natural reinforcement mechanism(stigmation) with positive feedback. This positive reinforcementlearning mechanism suggests a self-organizing system.

There are other ways for insects to communicate with each other. Someants and bees have developed ways of communicating with their nearestneighbors about food sources, for instance, to get help with oraltruistically share information with the group. This nearest neighborcommunication approach, which is primarily sense based, is key to theformation of flocking, herding and schooling behaviors in animals andfish.

In the case of bees or ants, there may be specialists that performspecific functions in the hive or nest in order for the wholeorganization to function more smoothly. This division of labor hasevolved for millions of years as an efficient social system.

Insects may communicate with each other indirectly. The process ofstigmation operates with an insect affecting, or changing, theenvironment, which then catalyzes other insect behavior. The use ofpheromones illustrates this process because the ants lay down anattracting chemical that may be acted upon by others in a limited time.

Animal and insect group behaviors emerge at the local level. Thoughinsects are not intelligent in some ways, their complex group behaviorssuggest that they have evolved social intelligence. By working ingroups, they have defended against predators and survived in hostileenvironments. But here are limits to this kind of swarm intelligence.

Though they have an initiator, most swarm or flocking behaviors do nothave a single persistent leader. Instead, such social behaviors focus onlocal and reactive interactions.

Flocking is a case in point. Each bird in a flock has limitedinformation about flockmates. Instead, they have neighbors they providelocal information on direction and speed. The big challenge is to avoidcollision with neighbors even as they signal trajectory and velocitydata through their behavior. Consequently, both attractive and repulsiveforces are involving in flocking behaviors.

There are, then, simple flocking rules that are useful to MRS designersbecause they illustrate local reactive behaviors: (A) Fly at a steadystate speed of neighbors, (B) Tend to the center of the flock and (C)Avoid collisions with neighbors. This is similar to driving on highwaysbecause we have limited information (visibility) restricted primarily toour nearest neighbors, with which we seek to avoid collision but alsomaintain a consistent pace. Flocking, like herding, school and swarmbehaviors, have evolved to allow groups of insects, birds, fish oranimals to move in a hostile environment while avoiding peripheralmembers from being picked off by predators. In nation, then, avoidanceof obstacles, neighbors and predators has become integrated into therules of survival that social group behaviors maintain.

How does a swarm form? An event will stimulate an individual insect toattract neighbors to the swarm activity. Though any individual can be aleader that initiates action, the recruitment of other individualsthrough attracting the cooperation of similarly interested neighbors iskey to the process because these individuals then respond by attractingmore neighbors, and so on. Thus, any individual can initiate a swarm orflock; this initiation is a sort of initial request to procure resourcesfor a specific (defensive or offensive) function or activity. Ratherthan a centralized mission control issuing orders to the troops,specific decentralized individuals can trigger group activity in a sortof local reactive chain reaction process that has the effect ofoverwhelming an enemy. In some cases, specialists alone, such as soldierants, may swarm for an attack process.

Insect and animal social behaviors are important to understandingcomplex social processes involving simple individuals. Attempts havebeen made to emulate biological system swarm intelligence fordevelopment of artificial systems of robots. For instance, Arkin's(1998) use of Brooks' simple modular reactive robot for group behaviorshows an attempt to model complex behaviors from simple robots.

Beyond Flocking: MRS Aggregation and the Formation and Reconfigurationof Dynamic Coalitions

The present invention goes beyond these interesting biologicalemulations. Because our system is layered, with simpler default modes ofoperation, we will use simple swarm behaviors in an MRS that employreactive local interactions. But the main objective is development ofcomplex aggregated MRS systems that are capable of intelligent socialbehavior as well as the operation of dynamic coalitions. Whereas thesimpler group behaviors have severely limited computation andcommunication resources in a homogeneous system, the present inventiondoes not. Simple swarm behaviors have anonymous homogeneous simplemembers (in uniform roles) with primitive local communication, minimalcomputation capacity and the limits of reactive behaviors using a narrowset of rules for learning and action. The limits of this biologicallyinspired system can be improved by development of an advanced MRS thatexhibits social intelligence. Our system has autonomous individual MRAswith highly advanced computation, AI and communications capabilities,complex learning and simulation functions, specialization features andteam behaviors in a heterogeneous system. In short, the presentinvention emulates human social behavior by using artificially thinkingmobile robotic agents for a range of functions.

The problem of how to aggregate objects is an important one in computerscience. Methods of aggregation involve collecting together disjointsets for an organized assembly. Combinatorial optimization is themathematical field concerned with seeking solutions to aggregationproblems. Aggregation is useful for mass pooling of customers withcommon interests for wholesale discounts. Similarly, combinatorialauctions are a useful commercial structure to enable parties to acquirebundles of items for optimum benefit.

For the purposes of the present invention, aggregation is important as aprocess for organizing groups of MRAs within an MRS. We are not onlyinterested in how groups of intelligent robotic agents form, but alsothe process by which groups break into subgroups and reform. Intelligentaggregation of MRAs involves automatic selection, formation,combination, reformation and dissipation of groups. Each new set ofintelligent agents represents a new configuration. Emergent behavior ofthe MRS leads to a complex self-organizing system that never settles onan equilibrium because it is constantly changing. Finally, unlike otherpure computational contexts for aggregation, the application in an MRSinvolves the geometry of space and extension and the physics andmechanics of motion.

The autonomy of intelligent agents leads to the opportunity forindividual specialization. Whether in biological or economic systems,specialization affords the optimization of teams because it establishesan efficiency enhancing division of labor. Groups of MRA specialists canwork together in an artificial system for increased benefit to theobjectives of the whole group. The existence of specialization alsomakes possible the interactions of sets of agents.

Aggregation is a process of grouping entities together. One useful wayto model groups is with game theory. As applied to an MRS, gametheoretic models have a geometric dimension. Game theoretic approachesto modeling an MRS is useful particularly because they can bemulti-phasal and interactive. Not only are MRA interactions nicelymodeled but complex interactions between sub-groups can be moreoptimally represented as well as interactions with the environment. Gametheory can model cooperating agent behavior as well as conflicting orheterogeneous behaviors. An example of a heuristic for MRS gametheoretic modeling parallels chess playing maneuvers, with openings,gambits and traps providing MRA models for the inter-operation ofartificial societies. Robotic agents work together to develop winninggame strategies for achieving goals or solving problems.

One of the aims of the present invention is to develop methods for MRAsto constantly develop shifting groups. We are interested in discoveringhow intelligent autonomous robotic agents form and reform into dynamiccoalitions of collectives. Understanding precisely how sub-groups ofMRAs organize, self-configure, reconstitute, adapt to their environmentand regroup is the key to understanding complex emergent group behaviorin intelligent self-organizing systems.

With severe resource constraints, mobile agents will tend to behaveaccording to simple rules inspired from biological systems, with localand reactive control. But endowed with sufficient computation andcommunications resources, an intelligent MRS will be able to performmore effectively. One of the areas of improvement in the operation ofgroups of MRSs lies in establishing methods and processes for dynamiccoalition behavior.

Multiple squads containing specialized MRAs can work together by sharingsensor data, data analysis, computation, communications and decisionprocesses. Such multiple squads can form alliances and temporarycoalitions for specific missions with numerous applications to industry.When group resources are restricted, specific squads can operateautonomously with limited information and still perform its objectives.Further, higher priority squads can get more resources at crucial times.Squads can reconstitute by taking resources from the larger group forcontinuous dynamic coalition reformation so as to more optimally adaptto changing environments. The existence of multiple micro-coalitions canbe better suited to satisfying multiple goals simultaneously and thusincrease the chances of a mission success.

Squads of MRAs break off from larger groups in an MRS. The squads canshare the larger computation, communication and sensor resources anddecision processes of the larger group. In effect, the squads operate asteams of nodes in a neural net that constantly reconfigures on the fly.Since some of the sensors in some of the squads are exogenous to eachteam, the squads have access to data streams beyond any limited team.Sub-teams are synchronized into the distributed network using hybrid AIapproaches. Nevertheless, each squad, and its reconfiguring teammates,can work independently with local behaviors. In addition, differencesbetween agents in a squad, for example, specialists or different“personalities,” can create complexity in squad behavior within thepractical constraints of their programming, as they inter-relate indifferent configurations. Squads self-select into various coalitionconfigurations, but during complex missions, new squads can pick upstragglers from previous damaged squads. Similarly, squads can merge ininstances where combined strength is needed to solve a problem. Roboticagent nodes can be added or subtracted as the system continuouslyreconfigures to achieve optimum success.

Different methods are employed in order to realize group MRAself-organization processes. In one important sense, game-theoretic andcellular automata simulations are useful in order for collectives in anMRS to map out and achieve complex plans for problem solving. Byemploying these processes within AI driven computation, intelligent MRAswork together to optimize complex processes in order to achieve missionsuccess. The opportunity to simulate these processes of constantre-grouping for dynamic coalitions of MRAs allows a new generation ofapplications of MRS social behaviors to be possible. In this way, amongothers, the present system far surpasses prior approaches to emulatingbiological social behaviors. Our system allows intelligent MRAs toconstantly shift in dynamic coalitions that are best suited forenvironmental interaction. It is precisely the continuously changingenvironment that requires development of a complex system that makespossible continuous reorganization.

Innovations and Advantages of the Present System

The present system has a number of innovations and advantages overearlier inventions. These innovations involve (1) multi-robotic systemarchitecture, (2) computation resource structure, dynamics andallocation, (3) AI dynamics, (4) group negotiation, learning anddecision structures and processes, (5) intelligent social behaviorinvolving mobile robots and (6) dynamic coalitions of MRAs.

The present invention utilizes a novel hybrid MRS architecture thatdynamically adjusts from manual operation of groups of MRAs to whollyautomated socially intelligent MRAs in order to accommodate severeresource restrictions as well as extremely complex behaviors. Bydefaulting to the most complex appropriate resource level, the systemoptimally adjusts to environmental conditions. For instance, very smallMRAs may be resource constrained and would thereby employ simpler localreactive behavioral rules. The architecture of the present system isalso both modular and scalable so that growth or shrinkage will notaffect performance.

The present system uses a distributed wireless grid supercomputingmodel. This approach allows the sharing of computation resources,including memory, database storage and data analysis capacity, therebyfar extending previous constraints. In addition, this distributed modelis optimal for equal node parallel processing within a collective.Computation processing speeds of dozens of teraops could be maintainedin this system, thereby providing ample resources for complex groupbehaviors. The present system also uses advanced routing procedures tomaximize the most efficient geodesic heuristics.

The present system employs a novel use of a MAS within a MRS in order tocommunicate, negotiate, control and organize group behaviors.Intelligent mobile software agents (IMSAs) are the analyticalrepresentatives that perform critical internal functions in the roboticsystem. In addition, intelligent negotiation agents (INAs) represent acore and innovative aspect of the present system as a vehicle for MRAsto interact and solve problems.

The present invention uses a dynamic reconfigurable evolutionary A-NNthat provides optimal adaptation to the changing environments of anintelligent MRS. The A-NN uses hybrid AI techniques, includingcombinations of GA, GP, FL and EC. As nodes are added or subtracted tothe network, the A-NN is automatically rewired for maximum efficiency.The system uses feedback loops to learn. The A-NN is useful to train thesystem in group learning processes. These applications to a mobile anddynamic MRS are novel. The use of connectionism (neural nets) in a MASand a MRS is a huge leap from earlier systems.

In order for the present system to learn, it employs FL processes thatuse probabilities to make group decisions by selecting the bestavailable option among a range of contestant options. The systemutilizes combinatorial optimization approaches to select the bestsolution to solve problems. Particularly in conflicting situationsbetween agents, there is a need to negotiate a settlement by developinga method of winner determination. The system employs novel approaches toasymmetric problem-solving by using multi-lateral negotiation methods.

The present invention uses game theoretic approaches and cellularautomata schemas in order to simulate tactical system opportunities foran MRS in novel ways. By using real-time CA and GT simulations, an MRScan automatically select an optimal problem-solving path and, hence,model complex interaction dynamics among MRAs and between MRAs and theenvironment. Given limited information in challenging environments withresource constraints, the use of simulation modeling for action planningand contingency scenario testing is necessary to achieve highlyintelligent MRS behavior.

The present system is novel because it is heterogeneous. The MRS employsspecialty robots for diverse functions. Some MRAs may have multiplefunctions, alternative functions or work in teams with complementaryfunctions. This approach increases efficiency of task execution becauseit promotes an automated division of labor in an MRS.

Despite their specialty functions, any agent can initiate groupbehaviors. The attraction of MRAs to collectives can be demand-initiatedin a novel implementation of group behavior in an MRS. This approachenhances system performance. In one implementation, stronger data inputsmay constitute invitations to act beyond a specific threshold andthereby initiate MRA grouping behaviors. The present system uses novelgroup attraction initiation methods.

The present system synthesizes local control with deliberative planning.This hybrid architecture is novel and is possible only with the uniqueconvergence of advanced computation technologies disclosed herein.

The present system uses novel approaches to dynamic coalition formation.Using these approaches, the MRS constantly reconfigures its structureand dynamics in order to adapt to environmental changes. This moreeffective adaptation provides increased speed, precision, efficiency andeffectiveness in mission critical situations.

By applying distributed artificial intelligence approaches, the presentsystem develops a way for groups of robotic agents to make decisions incooperative and in conflicting situations in real time. This is a noveland important advance over earlier systems.

The present system implements novel MRS approaches involving tacticalcooperating teams of MRAs. This sophisticated use of the systemtranscends earlier notions of artificial group intelligence.

Why are groups of robots important? Traditionally, robot groups allow anincreased speed to do a task. Like in nature, groups are increasinglyreliable since some may fail but the group still finishes the task. Inaddition, using robot groups to perform tasks can be more flexible thanonly individual robots. The present system offers higher performancebenchmarks for these traditional advantages.

Since the present system uses multiple hybrid architectures, at thesystem and AI levels, there are nontrivial advantages over earliersystems.

The present system most efficiently implements complex group behavior inan artificial robotic system. For example, unlike earlier artificialsystems that seek to emulate insect behaviors, the present inventionseeks to emulate, and transcend, complex human group judgment to developa true social intelligence. Consequently, the present invention goesbeyond robotic systems that focus primarily on local control of thenearest neighbor and reactive behaviors.

The use of coordinated, cooperative and reconfiguring squads in dynamiccoalitions in the present system provides numerous novel and usefuladvantages.

Finally, the present system is useful for a broad range of importantapplications, from manufacturing to toxic clean-up and from remoteexploration to traffic coordination. The sheer breadth of collectiverobotic applications, to industry and beyond, using the present systemsuggests a range of uses that could provide revolutionary implications.

Applications of the Present Invention

There are numerous applications of the present system. Robots can havespecific functions for specialized purposes. One robot can clean, whileanother can dry. But specialized robots can have particularly highutility as they function in teams. While specific purpose robots areuseful, multiple function robots are increasingly productive. Multiplefunction robots can switch roles or change forms as needed to completecomplex tasks. The more tasks a robot can do because of its multiplespecialties, the more plasticity and flexibility it has.

Multi-functional teams of robots can perform more tasks than specificspecialty robots. The more tasks that robots can do, the more plasticityof tasks a team of robots can perform because of the efficiency benefitsof the maximized division of labor.

The following is an extensive (but not exhaustive) list of applicationsof groups of robots that the present invention advances.

Manufacturing

The present system enhances factory production, assembly anddistribution processes. Methods for groups of robots to work togethermay greatly accelerate production techniques. For instance, by usinggroups of multi-functional autonomous robots, a host products can beproduced faster, more efficiently and cheaper than with earlier methods.

Regarding the factory assembly process, the novel use of groups ofautonomous mobile robots may reshape the very idea of an assembly linebecause new interactive processes, reflecting an efficient modularworkspace, will reconfigure approaches to activities in which parts arecombined to a whole. The application of self-organized groups ofmultifunctional robotic systems to manufacturing assembly can promotejust-in-time production processes and lean inventory to save time andincrease efficiency.

The distribution function of factories, such as loading and unloading,can be improved with teams of autonomous robots working together. Such asystem can replace routine labor practices.

Construction and Repair of Structures and Roads

Self-organizing teams of autonomous robots can build and repair roadsand structures. From laying track or pipe to electrical, plumbing,framing and roofing, an MRS can be useful in performing laborioustime-intensive routine structure building construction functions.Similarly, MRAs can be useful in the repair of buildings and streets.These novel MRA processes can save time and reduce costs of buildingconstruction as well as road work and repair. In one practicalapplication, pot holes can be automatically detected and repaired byteams of MRAs.

Medical Applications: Medi-Bots

There are two categories of application of the present invention to themedical field. First, groups of medical robots (medi-bots) can be usedin critical field situations to stabilize a patient. Autonomousmedi-bots work together to (a) diagnose a patient's trauma, (b)resuscitate, via electronic pulse or CPR, a patient whose cardiac orpulmonary functions have ceased, (c) cauterize wounds to stop (orminimize) bleeding, (d) apply an IV for intravenous solutiontransmission in order to replace vital fluids and (e) call for moremedical resources by providing a precise physical location position.Multiple medi-bots can much more efficiently rescue and stabilizepatients, thereby saving lives.

Second, groups of medi-bot can assist doctors in clinical situations byperforming functions typically attributed to nurses and assistants. Suchmedi-bots can monitor patient functions during procedures as well asactively support the surgeon or dentist so as to save time. Thesemedi-bots can also supply expertise in critical operating roomenvironments. In critical emergency room situations, where time andprecision can make a difference, medi-bots can save lives.

Reconnaissance and Surveillance

A big category of use of the present system lies in reconnaissance andsurveillance. Multiple autonomous robots working as a team are optimalfor reconnaissance and surveillance activities. These MRAs can transmitreal-time vision and sound to off-site locations, typically viasatellites or terrestrial communications systems.

In one mode, the MRAs can be very small micro robots (more fullyreferenced below) that provide stealth advantages for reconnaissance andsurveillance purposes.

In other embodiments, MRAs can be disguised as natural phenomena, suchas animals, birds, insects, etc. for evasive and stealthy advantages. Byemulating natural animal behaviors, mission effectiveness can bemaximized.

Finally, by using groups of MRAs, a more complete, more dynamic and moreaccurate view of the terrain being viewed can be maintained than withany other existing technology.

If captured, an MRA in this system can erase its programming and berendered a useless pile of sensors, while the remaining network nodesautomatically reconfigure for effective performance.

Search and Rescue

Including reconnaissance MRAs and medi-bots, teams of robotic agents canconduct search and rescue operations in difficult terrain that may beinhospitable to humans, such as in extreme weather.

Toxic Clean-Ups

Groups of MRAs can be used to perform complex clean-up operations thatmay be hazardous to humans. These clean-up categories include: (a) toxicwaste dumps, (b) nuclear reactor cleaning, (c) oil spill events and (d)sewer cleaning.

MRAs can use self-organizing maps of a local terrain to devise plans tomost efficiently and safely provide toxic clean-up operations, therebysaving lives and protecting the environment.

Fire-Fighting

Using similar configurations and methods as used in toxic clean-upapplications of MRAs, an MRS can be used to fight fires. Ground MRAs candig trenches and plot trajectories for the expanding fire territory,while aerial MRAs can drop fire retardant at tactical locations foroptimal effect. As with toxic clean-ups, MRAs use self-organizingmapping processes to assess the scope and dynamics of the full-motionfire situation. Fire-fighting MRAs can save lives and protect property.This application can be useful for forest fires, urban fires orindustrial structure fires that require complex problem solving anddecisive action. Medi-bots can be used in conjunction with these firefighting applications for maximum benefits.

Mining

MRA teams can be very useful for mining minerals in remote locations.Robots can identify the most promising locations to dig and then helpwith laborious digging and sifting tasks. Groups of MRAs can work fasterand more efficiently than current automation processes, in part becausethey are mobile, autonomous and self-organizing.

Agriculture

Farming has enjoyed increased automation processes for generations so asto maximize production. Groups of MRAs can continue this automationevolution, particularly in the planting and harvesting contexts in whichgreater care is required for specific crops such as fruit andvegetables. In general, MRAs replace the routine functions of migrantpickers.

Ship Hazards

Like toxic clean-ups, ships have a number of complex and dangerousproblems to solve. Because ships function as self-enclosed physicaldomains, MRAs can operate effectively on specific problems. Groups ofMRAs can provide effective automated solutions to hazardous functions,thereby reducing risks and saving time and money.

Clearing Minefields

One main activity for MRAs involves demining. Groups of autonomousrobots can work together to either dis-assemble or explode mines thatare discovered in a self-organized search process. In addition,disarming bombs can be a useful function for groups of MRAs.

Traffic Coordination

Groups of automated vehicles can use the present system for effectiveoperation. MRS vehicle categories may include cars, trucks, trains,aircraft and ships. In particular, cargo may be moved on various groupsof autonomous vehicles for greater efficiency, timeliness andcost-benefit. Such traffic coordination systems may develop complexrouting algorithms that emulate, and transcend, bird flocking or antforaging behaviors.

Elevator and Dam System Regulation

Systems of elevators can be better guided and coordinated by usingautonomous group logic. Similarly, dams can be regulated moreefficiently by using group logic processes of an MRS.

Weather Prediction

The present system is useful to organize groups of weather balloons oraircraft to gather and disseminate data. The MRS is ideally suited tocomplex adaptive environments such as detecting dangerous weatherconditions such as tornados or hurricanes. Groups of self-organizingMRAs can more rapidly predict dramatic weather system changes.

In an active mode, MRAs can not only predict poor weather but caninfluence its outcome. In a drought situation, MRAs can seed clouds toincrease the likelihood of inducing rain. In an extreme case, MRAs canprevent tornadoes by influencing their movement very early in theirdevelopment and changing the immediate environmental conditions. Onlyself-organizing groups of automated mobile robotic agents with specificfunctions—such as warming cool air in limited areas so as to retard orminimize a turbulent cyclic force—could execute this precisely orrapidly.

Satellites

Groups of satellites can work together to perform distinctive functionssuch as optimally tracking moving objects by using the present system.

The present system can also be used to have groups of self-organizedautonomous MRAs repair or readjust a satellite remotely.

Underwater Applications

As with other remote domains, the present system can be used inunderwater applications. Specifically, the underwater context can beused with other applications, including surveillance, reconnaissance,search and rescuer and demining.

Remote and Space Exploration

The use of the present system for space and remote exploration islogical. By using teams of self-organizing MRAs, complex explorationactivities can be routinely performed. This technology can be applied tounderwater, extreme cold or deep space missions which are optimized forthe flexibility and efficiency of the group behavior of mobile roboticvehicles. These vehicles can have multiple functions for the collectionand analysis of local environmental data. In some situations, these MRAscan conduct covert operations during which they may need evasiveprogramming capabilities.

Sentry Protective Services

Groups of MRAs can be used as an automated system of sentries forsecurity protection purposes. Sentries can be used not only forsurveillance but also for defensive uses in order to protect structuresor personnel. Such MRA sentries detect and respond to invasive action byunauthorized personnel by tracking and evading the intruders and callingfor assistance. In a more aggressive mode, automated sentries canrespond to invasive behaviors by disarming and subduing unauthorizedactivities until the authorities can arrive.

Cinematography

The present system can be used by groups of MRAs that operate video orfilm cameras in order to capture dynamic movie scenes. Because the MRAscan be constantly moving and can be both self-organizing andsynchronized, an MRA can facilitate a new generation of film-makingtechniques, particularly for the popular action sequences. While movingin synchronized or random ways, MRAs are well suited to capture movingscenes in distinctive cinemagraphic ways only possible in an MRS.

Commercial Laundry or Restaurant

Routine restaurant food preparation and delivery and commercial laundryfunctions can be done by teams of MRAs. Working as a group of shortorder cooks, MRAs can produce more variety of recipes in a shorter timethan professional chefs or waitresses. Similarly, a commercial laundryservice can be optimized by using groups of MRAs to organize, clean andpackage clothes. One hour discount cleaning is now possible by using anMRS.

Micro-Robotics

One of the most exciting developments in robotics is the advent of small(fly-sized) robots. But the smaller the robot the greater utility isderived from working in groups. Once in groups, micro-robots can becomevery useful much as ants or bees are successful in groups. A number ofgroup robotic applications involve the use of micro-robots. Givenresource constraints of micro-robots, the group gains massive resourcebenefits while operating in a network using the present invention thatmake possible dramatic performance gains over merely a collection ofunlinked autonomous robotic agents. These MRS micro-robotic networkscould also be construed as very small scale integrated systems (VSSIS).

Generally, the smaller the micro-robotic agent, the simpler the system.Hence, some straightforward applications include surveillance andreconnaissance in which sensor data is transmitted for central use whilethe system is camouflaged as a natural phenomena (such as a real fly orspider).

Teams of self-organizing micro-robots utilizing local reactiveoperational behaviors can use traditional computer based group behaviorthat emulates biological system behaviors such as foraging or flocking.But the present system strives to go beyond these restrictive behaviors.

In one embodiment, disaggregated collectives of micro MRAs can formtogether into a larger composite robot exhibiting unified behavior. Thisis important so as to allow larger robots to disassemble intoconstituent (specialized) parts if necessary in order to evade apredator or disguise a maneuver.

In another embodiment, micro MRAs using the present system could inspectand assemble micro-electronic systems or could inspect biologicalentities for abnormalities.

As microprocessors progress to ever smaller sizes and greatercapabilities, the practical uses and possibilities for micro-robotics,particularly in self-organizing groups, increases dramatically.

Nanotechnology

A close relative of micro-robotics is nanotechnology. The use bynanorobots and nanoprobes of the present system is logical. The sameargument and restrictions of microrobotics apply to molecular sizednanorobotics. Like microrobots, nanorobots can assemble into largercomposites that themselves work together as autonomous groups.

Uses of nanorobots include surveillance and reconnaissance. But morefanciful uses include biological applications that include cleaningarteries by injecting a group of nano-MRAs into a patient's bloodstream. The nanorobots will go to the affected area, perform theoperation internally and regroup for extraction. Nanorobots could alsobe used to identify and repair microelectronic abnormalities.

Expert Systems

Groups of anthropological MRA “androids” can work together to formcomplex expert systems. Operating as consultants with autonomousopinions, robot expert groups can behave like specialist teammates tocollect and analyze data, perform forecasting, develop alternativescenarios, make predictions and give advice in the form of reports. Suchgroups of expert consulting opinions can involve numerous substantiveindustry categories and topics, including optimal telecom and energyrouting algorithms and economic, business industry and scientificanalyses. The personalities, experience and learning processes of theandroid MRAs evolve. Taken together, such expert systems constitute athink tank. Ultimately, such a group of autonomous self-organizingrobotic agents can form and reform coalitions of specialist expertssimilar to a sophisticated consulting firm. By applying evolutionarylearning and combining various opinions, such complex systems can becreative and capable of original thinking approaches that far surpasschess playing supercomputers.

MRAs

MRAs can take numerous forms. Since there are numerous applications ofthe present system in divergent industrial and technical contexts, it isappropriate to identify the structure and function of the variety ofMRAs that can perform various jobs.

MRA vehicles can include various forms of aircraft, such as airplane,glider, helicopter, balloon, blimp, satellite or spacecraft. MRAs canoperate in water as ships, boats, submarines or hovercraft. On land,MRAs can be automobiles, trucks, farm equipment, mining equipment,factory equipment, etc. There may be entirely new forms of MRAs as well,such as remote exploration devices, anthropological androids,micro-robots intended to emulate insect appearances, nano-robots and soon. The range of sizes and forms of MRA are very broad.

What unites the MRAs in the present system are common processes thatmake possible self-organizing group behavior of autonomous intelligentmobile robots. Nevertheless, the various specialized applications thatare made possible by using the present system allow a broad range ofimportant uses that endeavor to enhance the human condition byperforming the riskiest, most remote, most complex, most routine andmost important tasks imaginable.

Reference to the remaining portions of the specification, including thedrawings and claims, will realize other features and advantages of thepresent invention. Further features and advantages of the presentinvention, as well as the structure and operation of various embodimentsof the present invention, are described in detail below with respect toaccompanying drawings, like reference numbers indicate identical orfunctionally similar elements.

List of Acronyms:

-   MAS: Multi-agent system-   MRS: Multi-robotic system-   MRA: Mobile robotic agent-   INA: Intelligent negotiation agent-   IMSA: Intelligent mobile software agent-   AI: Artificial intelligence-   D-AI: Distributed artificial intelligence-   A-NN: Artificial neural network-   E-A-NN: Evolutionary artificial neural network-   FL: Fuzzy logic-   GA: Genetic algorithm-   GP: Genetic programming-   EC: Evolutionary computation-   OS: Operating system-   CA: Cellular automata-   GT: Game theory

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a list of system layers;

FIG. 2. is a schematic diagram of a synthetic hybrid control system foran MRA;

FIG. 3 is a table of a dynamic database organization;

FIG. 4 is an illustration of three MRAs identifying MRA locations withsensors;

FIG. 5 is a diagram of an MRA assessing its environmental situation andcoordinating change in state;

FIG. 6 illustrates a diagram of a metacomputing model for distributedMRS in which flexible mobile grid architecture is organized into dynamicclusters;

FIG. 7 is an illustration showing the sharing of computation resourcesamong MRA nodes in a wireless mobile MRS, including the efficientrouting of database and analytical functions;

FIG. 8 is a diagram showing database coordination in a distributed MRS;

FIG. 9 is a diagram showing a dynamic distributed object relationaldatabase data flow process;

FIG. 10 is a diagram showing temporal objects in an object relationaldatabase management system;

FIG. 11 is a diagram showing mobile grid dynamics;

FIG. 12 is a diagram showing autonomous blackboards for MRAs;

FIG. 13 illustrates a diagram showing intelligent mobile software agentsoperations control in MRAs;

FIG. 14 is a flow chart showing MRA juvenile and adult training levels;

FIG. 15 is a diagram showing MRA attitude biases;

FIG. 16 is a flow diagram showing the learning and adaptation fromenvironmental interaction;

FIG. 17 is a flow diagram showing the MRA training process;

FIG. 18 is a flow diagram showing reinforcement learning;

FIG. 19 is a flow diagram showing hybrid learning with time constraints;

FIG. 20 is an illustration of social learning in which MRAs learn fromother MRAs;

FIG. 21 is an illustration showing MRAs that teach other MRAs;

FIG. 22 is an illustration showing asymmetric MRA leadership and theemergence of temporary hubs;

FIG. 23 is an illustration showing specialized learning inself-organizing teams;

FIG. 24 is an illustration showing automated specialization in whichself-organization by task division occurs for individual specialization;

FIG. 25 is a flow diagram showing a self-organizing map;

FIG. 26 is a flow diagram showing a genetic algorithm;

FIG. 27 is an illustration showing a binary genetic algorithm;

FIG. 28 is an illustration showing a genetic programming treearchitecture;

FIG. 29 is an illustration showing parallel subpopulations fitnessevaluation;

FIG. 30 is an illustration showing a two layer neural network;

FIG. 31 illustrates an artificial neural network connection weights;

FIG. 32 illustrates genetic programming in the calculation of initialweights;

FIG. 33 illustrates genetic programming applied to indeterministicartificial neural networks;

FIG. 34 is an illustration showing an evolutionary artificial networkconnection and node additions;

FIG. 35 illustrates evolutionary indeterministic artificial neuralnetwork feed forward progress;

FIG. 36 illustrates an evolutionary search for connection weights in anANN;

FIG. 37 is a flow diagram showing a fuzzy logic module;

FIG. 38 is an illustration of a neuro fuzzy controller with two inputvariables and three rules;

FIG. 39 illustrates a five layer evolving fuzzy neural network;

FIG. 40 illustrates an adaptive network based fuzzy inference system;

FIG. 41 illustrates a self-organizing neural fuzzy inference networkarchitecture;

FIG. 42 illustrates a dynamic evolving fuzzy neural network;

FIG. 43 illustrates a flexible extensible distributed ANN in which ANNcomputation is shared between MRAs;

FIG. 44 is an illustration showing intelligent mobile software agents(IMSA) dynamics in a multi-agent system with an emphasis on MRAinteractions;

FIG. 45 is an illustration showing IMSA relations between MRAs;

FIG. 46 is a flow diagram showing the operation of analytical agents;

FIG. 47 is a flow diagram showing the operation of search agents;

FIG. 48 is a flow diagram showing the initial operation of intelligentnegotiation agents (INAs);

FIG. 49 is a flow diagram showing IMSA intercommunications;

FIG. 50 is a flow diagram showing INA architecture;

FIG. 51 is a flow diagram showing the pre-negotiation process;

FIG. 52 is a flow diagram showing INA logistics;

FIGS. 53A and 53B are a flow diagram showing negotiation in adistributed system with mobility;

FIG. 54 is an illustration showing the simultaneous multi-lateralnegotiation process with multiple variables;

FIG. 55 is an illustration showing multivariate negotiation factors;

FIG. 56 is a flow diagram showing winner determination in a competitiveINA framework;

FIG. 57 is a table showing the argumentation process;

FIG. 58 is a flow diagram showing anticipation of opposing INAstrategies;

FIG. 59 is a flow diagram showing problem identification in which agroup of MRAs agree to narrow focus;

FIG. 60 is a flow diagram showing solution option development betweenMRAs;

FIG. 61 is a flow diagram showing a solution option selection method;

FIG. 62 is a flow diagram showing how the MRAs select the best availablesolution to a problem in the present circumstance while waiting for morerecent relevant information;

FIG. 63 illustrates MRA group agreement;

FIG. 64 is a table that shows the temporal aspect of the decisionprocess;

FIG. 65 is a flow diagram showing the application of multivariateanalysis to problem solving;

FIG. 66 is a flow diagram showing the application of regression analysisto problem solving of conflicting MRAs for winner determination;

FIG. 67 is a flow diagram showing the application of pattern analysisand trend analysis to problem solving of conflicting MRAs for winnerdetermination;

FIG. 68 illustrates the modeling of MRS activity with simulations inwhich situation assessment is performed;

FIG. 69 is a flow diagram showing the synchronization of simulationswithin an MRA cluster;

FIG. 70 illustrates the contingency cellular automata (CA) scenariooption simulations;

FIG. 71 illustrates reversible CA projecting backwards from a goal;

FIG. 72 illustrates adaptive geometric set theory applied to an MRS;

FIG. 73 illustrates the optimal simulation selection in which simulationscenarios are (temporarily) converged;

FIG. 74 is a flow diagram showing the initiation of the aggregationprocess in which sets of MRAs form from the larger collective;

FIG. 75 illustrates the initiation of homogeneous MRA group formation;

FIG. 76 illustrates the initiation of common heterogeneous MRA groupformation;

FIG. 77 illustrates the initiation of complementary heterogeneous(specialized) MRA group formation;

FIG. 78 is a flow diagram illustrating the initial phase ofdemand-initiated environmental adaptation;

FIG. 79 illustrates continuous MRA group composition reconfiguration;

FIG. 80 illustrates the continuous reconfiguration of sub-networks;

FIG. 81 illustrates dynamic group behavior adaptation to environmentalinteraction;

FIG. 82 is a flow diagram illustrating the parallel dynamic travelingsalesman problem (TSP) with cooperating autonomous agents;

FIG. 83 illustrates the altruistic sacrifice of MRAs (gambit tactic) inorder to acquire sensor information to increase chances of overallmission success;

FIG. 84 is a flow diagram illustrating the general dynamic coalitionprocess;

FIG. 85 illustrates group MRA coordination and obstacle avoidance;

FIG. 86 illustrates specific MRA functionality via specialization;

FIG. 87 illustrates specialized MRAs working as a team;

FIG. 88 illustrates multi-functional self-organizing MRAs;

FIG. 89 illustrates surveillance and reconnaissance of a mobile objectsensed and tracked by multiple micro-MRAs;

FIG. 90 illustrates remote exploration with initial tracking of multipleobjects with multiple micro-MRAs;

FIG. 91 illustrates sentry behavior within limited perimeters;

FIG. 92 illustrates cinematography applications with MRAs in whichobjects are sensed and tracked;

FIG. 93 illustrates land based toxic site clean up with multiple MRAs;

FIG. 94 illustrates dynamic cleanup of an oil spill within limited hydroperimeters by multiple MRAs;

FIG. 95 illustrates fire fighting with multiple MRAs as a dynamicinteraction between the MRS and a complex environment;

FIG. 96 illustrates manufacturing production in which an object iscreated by using multiple MRAs;

FIG. 97 illustrates the assembly of objects in which parts are combinedto create a whole object using multiple MRAs;

FIG. 98 illustrates road generation using MRAs, and;

FIG. 99 illustrates surgical micro MRAs used for trauma intervention andstabilization.

DETAILED DESCRIPTION OF THE INVENTION

The present disclosures illustrate in detail the main ideas of thepresent system. Since the present invention has numerous embodiments, itis not intended to restrict the invention to a single embodiment.

The system and methods incorporated in the present invention areimplemented by using software program code applied to networks ofcomputers. Specifically, the present invention represents a multiroboticsystem (MRS) that includes at least two mobile robotic agents (MRAs).These MRAs have various useful purposes in the context of industrial andpractical applications. The MRAs use complex software program code,including mobile software agents, to execute specific instructionsinvolving robotic and computation operations. The software capabilitiesactivate specific robotic functions within MRAs involving movement anddecision-making.

The present invention focuses on how groups of autonomous MRAs operatein a distributed MRS. As such, the invention, or cluster of methods,solves problems in the area of computation for groups of mobile robotsin a distributed network. The system shows novel ways for groups of MRAsto work together to achieve specific goals such as mapping theenvironment, coordinating missions, aggregating into dynamic coalitionsand engaging in complex self-organizing activities. The system employshybrid models for collective robotic control that combines not onlysynthetic control methods that combine central and behavior-basedapproaches but also hybrid artificial intelligence methods. Distributedartificial intelligence approaches are used in several contexts of thepresent system, including learning, negotiation, simulation anddecision-making of MRAs and intelligent mobile software agents (IMSAs).

The main approach for decision making of MRA collectives isdecentralized. In order to achieve self-organizing aggregation forspecific missions in changing environments, the MRS engages in learningand decision processes that employ extensive use of IMSAs. IMSAsinteract with each other to handle routine matters between MRAs,including communication, analysis and negotiation. Intelligentnegotiation agents (INAs) provide a medium for multilateral interactionof MRAs for group decisions. Simulations are used extensively to modeland select optimal pathways for MRA group action and for the evaluationof scenarios for action.

This detailed description of the figures is divided into several partsthat explain: (1) the main structure and operation of the MRS, (2)resource management of a distributed MRS, (3) MRA learning, (4) AI andANN, (5) IMSAs, (6) INAs, (7) problem solving, (8) cellular automata(CA) simulations, (9) aggregation and self-organizing dynamic coalitionsand (10) specific applications including (a) remote sensing, (b) hazardmanagement and (c) building processes.

General System Architecture and Dynamics

FIG. 1 illustrates the layers of the multi-robotic system architecture.The first level shows a synthetic hybrid control system for MRAsincluding central planning control and behavior-based control aspects,which are further described in FIG. 2. MRAs are independent autonomousagents that use AI to interact with their environment using the hybridcontrol model.

The second layer is the level of the mobile robotic system in adistributed network which connects together individual MRAs usingcommunications. The Grid computing architecture is used to link the MRAstogether at layer three in order to share computation and databaseresources between the individual MRAs for maximum network efficiency. Inthis way, the MRA network develops dynamic clusters for optimalcomputation and storage capability. Particularly in time constraineddynamic environments, the mobile Grid network model is critical in orderto accomplish complex tasks.

At level four, the dynamic distributed database system is used. Thisextension of the Grid computing hardware architecture uses objectrelational databases and temporal data objects to organize data betweendatabases in the MRAs.

Artificial intelligence is used in layer five as a dynamic interactiveartificial neural network that evolves. By applying AI to evolvingnetworks of MRAs as they interact in a dynamic environment, complexlearning and adaptation processes develop.

Intelligent mobile software agents (IMSAs) operate within themulti-agent system (MAS), comprising the sixth layer. The IMSAs arecomplex agents that perform a number of important functions within eachMRA, such as analysis and decision-making, and between MRAs, such asdata search, negotiation and collaboration.

The MRAs produce complex simulations to represent their relativepositions and movements as well as to map out the possible scenarios forfuture action. These simulations are represented as mobile cellularautomata in level seven.

Finally, the specific functional application of each implementation ofthe system comprises level eight. The main application categories ofremote sensing, hazard management and manufacturing processes each usespecific functional representations that are closest to the environmentwith specific hardware types.

FIG. 2 shows a multi-layer architecture of an MRA synthetic hybridcontrol system. The first level shows specific central (0270) andbehavior-based (0280) control processes, in which the former usesabstract logic and the latter is reactive to the environment. In layertwo, the two main processes are intermediated (0260) in syntheticcontrol approaches.

Layer three illustrates several main hybrid control systems that combineboth central planning and behavior-based control models: (1) planningdriven (0220), (2) advice mediation (0230), (3) adaptation (0240) and(4) postponement (0250). The planning-driven approach to combining themain control methods determines the behavioral component; it isprimarily a top-down model. The advice mediation approach models thecentral planning function as advice giving, but allows the reactivemodel to decide; it is primarily a down-up model. The adaptation modeluses the central planning control module to continuously alter reactionin changing conditions. Finally, the postponement model uses a leastcommitment approach to wait to the last moment to collect informationfrom the reactive control module until it decides to act.

Finally, at layer four, the suite of synthetic control systems (0210) isconstructed of various combinations of these main hybrid control models.For instance, a robotic unit may use a suite of hybrid control systemsin order to optimize specific situations.

The evolution of these hybrid control models, as represented in thelayered structure of figure two, is suited to complex social behaviorsof a distributed MRS used in dynamic environments.

The structure of the dynamic database organization is referenced in FIG.3 as a table. A single MRA unit includes a hardware component with anobject-relational database. Within this MRA, software agents performtasks such as analysis, negotiation and decision-making. On a moreadvanced level, a single MRA has complex computation resources tomanage, including AI and ANN.

Taken as a whole system of MRAs in a distributed network, the MRAsmanage data within a network and share database organizationalfunctions. Similarly, in the distributed network, the software agentsbecome mobile and interact with other software agents at various MRAlocations. Finally, on this network level linking MRAs, computationresources are constantly restructured so as to maximize computer powerfor complex time constrained applications.

On the level of mobility, MRAs change spatial positions in variable timesequences in order to perform specific tasks. The software agents arealso mobile within a limited wireless range between mobile MRAs. Thenetwork of MRAs constantly rewires its computation resources by using AIand ANN in order to adapt to its environment and to optimally performthe collective mission.

Thus FIG. 3 shows that while a single autonomous unit is important, whencombined with other similar units in a network and provided withmobility, and when also combined with both software agent systemintegration and AI and ANN capabilities, the system produces a complexadaptive collective capable of autonomous mobile interaction.

FIGS. 4 and 5 show simple MRA operations such as using sensors to locateother MRAs or changing position by avoiding obstacles. FIG. 4 shows asimple communication between three MRAs using sensors. Each MRA uses itssensors to detect the positions of the other MRAs. In this way, each MRAcan identify each others' positions. In another embodiment, the positionof each MRA may be transmitted to other MRAs in the network by way ofwireless communications. In still another embodiment, positions of MRAscan be transmitted to other MRAs by satellite, radar or other externalGPS tracking system. In these ways, the positions of MRAs can be trackedby other MRAs in the network. The reason that individual MRA positiontracking of other MRAs is important is that in a noisy environment,there are multiple methods for MRAs to track other MRAs. In the totalabsence of communication, an individual MRA may default to abehavior-based reactive mode of interacting with other MRAs and with theenvironment.

An individual MRA can detect an object (0520) in the environment withits sensors and change position from 0520 to 0530 as illustrated in FIG.5.

The individual autonomous MRAs are part of a distributed network in muchthe same way that inert computers are linked together into gridcomputing networks for supercomputing. This mobile grid computer networkcomprised of individual MRAs uses wireless communications in order toshare computation resources. FIG. 6 shows a metacomputing model for adistributed mobile robotic system (MRS). The figure describes a flexiblemobile grid architecture of dynamic clusters of mobile MRAs. At 0610,MRA 1 requests (at (1)(a), (1)(b) and (1)(c)) computation resources anddata storage capacity from other MRAs. MRAs 2, 3 and 4 (at 0620, 0630and 0640, respectively) then respond to the request (at (2)(a), (2)(b)and (2)(c), respectively) of MRA 1 (at 0650).

FIG. 7 illustrates the sharing of computation resources among MRA nodesin a wireless mobile MRS, with an emphasis on the routing of databaseand analytical functions. The distributed network of MRAs can worktogether as one dynamic unit. Messages are input to the report statusdistributor (0720) and the request coordinator (0730) The report statusdistributor feeds messages to the MRS (0740) which interacts with thecache (0750) and the data stream (0760). The cache also interacts withthe analytical (0770) functions of the system. Messages are output fromthe data stream and from the request coordinator. The mobile wirelessgrid computing architecture uses the most recent version of the messagepassing interface (MPI) for distributed computer networks. The use ofgrid architecture in a mobile wireless distributed network allows for amaximum of flexibility and scalability in providing massive resources inadaptive environments.

MRAs possess not only computation capability, which allow up to teraops(one trillion operations per second) or yodaops of system processingpower, but also database storage capacity as well. Each MRA possesses adatabase. However, taken as a whole, the MRS network comprises adistributed database system with complex coordination capabilities. Thedatabases work together to store data objects such as a table, acalculation, a multimedia segment or other complex combinations ofcoherent mobile code. Such working together involves sharing databasestorage among a number of machines in order to ensure maximum efficiencyunder severe time constraints. FIG. 8 shows database coordination in adistributed MRS. The front end (0810) inputs queries at the queryinitiator (0820) which inputs to the query executor (0830), which hasbuffers (0870) with other MRAs. The multiple data sources (0850 and0860) supply information to the query executor. The query executoroutputs its queries to output queues (0840) at various other MRAs(0880). This process is further illustrated in FIG. 9.

In FIG. 9, the dynamic distributed object relational database data flowprocess is described. The query origination (0910) moves to the variousdatabases (0920), DB1 through DB5, internal to MRA 1 through MRA 5. Thequery executor (0950) which is buffered (at 0970), searches the samedatabases (0980), which have sensor data stream inputs (0930) as datasources (0940). Once accessed, the databases output their data at theoutput queues (0990). This distributed model shows a parallel networkapproach to database organization. In one embodiment, the system usesactive storage databases in which the computer processing capacity isinternal to the database, which is itself continuously mining objectsfor analytical functionality.

One of the particular types of objects that the object-relationaldatabase management system organizes involves temporal objects. Becausethe MRS is typically time constrained in order to perform its primarymissions, temporal objects become a prominent part of the distributeddatabase system. Temporal objects reveal their temporal priority inorder to be listed in a higher or lower relative priority in thedatabase for storage retrieval purposes. Objects are “tagged” withtemporal priorities such as “now”, “imminent”, “very soon”, “in thefuture”, “possibly useful in the future”, “past”, “near past”,“immediate past”, “urgent priority”, etc. By storing, andreprioritizing, objects according to temporal priority, the system canoperate much more efficiently. FIG. 10 shows how temporal objectsoperate in a ORDbMS.

The query generator (1010) requests the query executor (1020) to accessdatabases at DB1 (1030) and DB2 (1050) in sequential order. Thesedatabases access the data object (1060), which is tagged as it undergoestemporal change and is given temporal priority (1040) and is thenprovided back to the query executor (1020). Once again the databases areaccessed with temporal information about the data object. The dataobject is then directed to the query manager (1070) for feedback to thesystem. By prioritizing data according to temporal priority, the systemcan route data efficiently and effectively anticipate functions.Temporal data is useful in the present system in the context of evolvinglearning, evolving ANN, evolving game theoretic negotiationapplications, evolving environmental conditions and general systemicadaptation processes.

FIG. 11 shows the mobile grid dynamics. Data sets at a specific locationinform the system analysis at 1120. The data sets are analyzed andinterpreted at 1110 in order to determine where the system should move.The system moves to the new position at 1130. Yet this change ofposition provides new data sets, which are, in turn, provided to thesystem for analysis in order to determine where the system should move.This dynamic process optimizes the functionality of the system.

It is necessary for MRAs to obtain and transmit information from otherMRAs about specific data such as physical position, analysis,negotiation and decision-making. Concise data sets are transmittedbetween MRAs in real time about the location and analytical state of theMRAs. These abbreviated data sets are consolidated in each MRA byautonomous blackboards, which act as “radar readouts” informing MRAsabout the state of the network.

In FIG. 12 autonomous MRA blackboards are described. In this example,limited information is referenced involving spatial position, vector andspeed so that each MRA can get a snapshot of the present situation ofevery other MRA in the system. In the figure, MRAs 1 through 4 readoutspecific data sets in a spreadsheet format at 1210 during phase one. Newdata sets are presented to the same MRAs in phase two to signify achange in state of the network. In one embodiment, an externalblackboard keeps track of the data as a form of back up. In the event ofa centralized blackboard on board a specific MRA, such as a satellite,the leader would maintain the consolidated information function. If sucha consolidated approach were used in a further embodiment of the system,the leader may shift, thereby providing fluidity for centralizedleadership of the system.

FIG. 13 describes the operation of intelligent mobile software agents(IMSAs) among MRAs. Though discussed below at FIGS. 44 to 58, IMSAs (andINAs) are the main software based methods for MRAs to communicate,interact and collaborate with each other. MRA 1 (1310) receives acollaboration agent sent by MRA 2 (1320), as it launches a search agentto both MRA 2 and 3 (1330). An interaction process is engaged betweenMRA 1 and MRA 2. Meanwhile, an analytical agent is launched by MRA 3 toMRA 1, while a messenger sub-agent is launched from MRA 3 to MRA 2.Finally, the figure shows negotiation agents (INAs) interacting betweenMRA 2 and MRA 3. These software based interactions represent a keymethod for MRAs to communicate and work with each other as a network.

FIGS. 14 through 19 deal with MRA training and learning, while FIGS. 20through 25 deal with social learning.

FIG. 14 is a flow chart depicting the evolution of training levelstates. After an MRA initiates a training exercise (1410), it increaseslevels of training (1420). It may employ a learning module with specificlearning tasks (1460) and refinement of learning tasks (1470) or it mayinteract with various environmental inputs (1430) in order to learn. Ata specific point, a juvenile training level is achieved (1480). However,with continued experiments with the environment, it improves learningwith positive reinforcement (1440) and an adult training level isreached (1450), which is constantly reinforced with a feedback loop.

MRA attitude biases are shown in FIG. 15. On a behavior spectrum betweenpassive (1510) and aggressive (1530) behaviors lies a moderate “normal”behavior (1520). With passive behavior, the MRA acts with slowerjudgment but generally with more information, while with aggressivebehavior, the MRA acts with faster judgment but within informationconstraints because of the time limits of quicker action.

Environmental interaction is critical for learning and adaptation. FIG.16 shows a flow chart in which MRAs interact with both other MRAs andwith the environment. After an MRA initiates a training exercise (1610),it either interacts with other MRAs (1620) or with its environment(1630). When it interacts with other MRAs, an MRA queries other MRAsabout a specific question (1640), while the MRAs then access databasesand respond to the data query (1660). Inter-MRA feedback is then sharedbetween MRAs for efficient learning (1680), akin to a tutorial. On theother hand, when an MRA interacts with the environment, as theenvironment changes, the MRA feedback changes (1650). In this case,negative feedback is avoided (1665) while positive feedback isattractive behavior (1670) which leads to reinforcement learning (1675)and a feedback loop with the environment. As the environment changes,new data about these changes is supplied to MRA databases in order forthem to access these environmental changes. When provided with positivefeedback, the MRA constantly updates its beliefs about the environment(1690).

The MRA training process includes a combination of environmentalinteraction with group sensor data as illustrated in FIG. 17. The MRAinitiates learning (1710) and accesses either the sensor data from otherMRA team members (1720) or the environment (1730). By accessing theenvironment direction, the MRA collects raw sensor data (1740). Whetherobtained from other MRAs or directly from the environment, the MRAanalyzes and interprets the sensor data (1750) and initiates a decisionto act based on the data (1760). In this way, training processes may beimplemented based on the data obtained, contingent on the method oforiginating the data (whether from the environment directly or fromother MRAs). Whereas FIG. 17 shows the two main ways of obtaining data,FIG. 18 shows the two main qualities of information, viz., intensity andquantity of data, which provide MRA learning reinforcement.

In FIG. 18, sensor data is input into an MRA (1810) while the intensityof inputs is measured (1820) or the quantity of inputs is measured fromdifferent sources (1830). In either event, the inputs are compared todatabases (1840) while each is provided a weighted value, with highintensity input weighting (1850) and quantity input weighting (1860),respectively. The MRA evaluates the weighted value from differentsources (1865) and interacts with the environment based on inputevaluation (1870). For instance, if a number of MRAs provide a largequantity of inputs that a mission objective is achieved, then theseinputs are weighted highly in order to provide reinforcement of aspecific behavior (1880); contrarily, if a very high weighting isassigned to an individual MRA data set because of the intensity of thedata, then this behavior is reinforced.

The combination of the aforementioned learning approaches present ahybrid learning model with time constraints illustrated in the flowchart of FIG. 19. Data from other MRAs (1910) and direct environmentalinputs (1920) are analyzed (1930) before the MRA acts (1940). The MRAthen proceeds to interact with the environment (1960) and receivepositive feedback (1950). This environmental feedback presents behaviorreinforcement in a minimal time (1980) and the MRA establishes a plan ofaction (1985), which is implemented by activating specific behavior(1990). Meanwhile, the MRA updates other MRAs (1970) which provides apartial feedback loop for MRAs to supply information for future sensordata.

In FIG. 20 social learning is described as MRA interaction. MRA 1(2010), MRA 2 (2015) and MRA 3 (2020) interact with objects (2030) inthe environment in an initial phase. In the second phase, the MRAsinteract with each other by sharing information about theobject-interaction. This descriptive phenomenology about the objects isused in the third phase by further interactions between the MRAs and theobjects.

FIG. 21 illustrates an MRA that teaches another MRA. MRA 2 (2120), a“student” with limited training, requests assistance from an experiencedMRA 1 (2110). While MRA 1 is in motion, and thus moves to a new position(2130), the “adult” MRA 1 provides the student with a learning modulevia a software agent.

Given the distributed environment of the present MRS network and thelearning schema presented, it is possible to have asymmetric MRAleaders. That is, if this is not a centralized system, it is stillpossible to have mission leaders, but they are not necessarilycentralized or even consistent. Like in a flock of geese, any member ofthe flock may be a leader, though temporarily. Consequently, asymmetricMRA leadership provides the emergence of temporary hubs of MRAs thatcluster together to interact with the environment.

FIG. 22 illustrates this process. In the first phase, a leader of acluster of MRAs (2210) interacts with a moving object (2220). But theleader is knocked out of action (2240) in the second phase, while a newleader emerges for the group (2230) as the new leader seeks the movingobject (2250) and it is also removed from action. Finally, in phasethree, yet another new leader emerges for the group (2260) while themobile object (2470) continues to elude the group. At each new phase, anew hub is created with a new leader of the MRA cluster. In each case,the goal is to seek out the elusive mobile object.

A division of labor can occur in specialized teams for increasinglyefficient performance as shown in FIG. 23. Each MRA is designated with aletter to signify its role as a specialist, while the whole groupinteracts with a mobile object (2320). In phase two, the MRAs reorganizeinto new positions in order to optimize the sharing of data andresources and to organize an interaction between the various specialistsand the object (2340). FIG. 24 further illustrates the self-organizationprocess by task division for automatic individual specialization. In thefirst phase, the group of MRAs (2410) interact with the object (2420).The MRAs automatically activate a specific specialization mode (2430) toattack the increasingly elusive object (2440) as shown in the secondphase. However, at phase three, the MRAs automatically reorganize to anew specialization mode (2450) to catch the object (2460).

One key application of the (social) learning and environmentalinteraction processes is to construct a self-organizing map of anuncertain environment. This map can then be used as a benchmark forfurther collective action. FIG. 25 is a flow chart that shows theprocess of a self-organizing map for a group of MRAs. After initialparameters are developed (2510), MRAs move to new locations to fulfill amission (2520), where they receive sensor feedback from the environment(2530). The MRAs create an initial map based on initial sensor dataorganization (2540) and obtain more sensor data (2550) as they covermore terrain to include in more refined mapping phases. In this way, theMRAs fill out the initial map to create a fuller picture of terrain toinclude formerly missing parts (2560). The MRAs can perform this fillingin procedure by using caching techniques that add the most recentinformation to a map outline. More complete data from sensors continueto refine the map (2570) as the MRAs continue to generate more andbetter data from continued mobility and data gathering. As objects inthe environment change position, the MRA sensor data inputs thatrepresent these changes continue to update the maps (2580).

Using artificial intelligence and artificial neural networks optimizesthe learning process. FIGS. 26 through 29 show the main AI procedures ofgenetic algorithms and genetic programming. These techniques are thenapplied, in FIGS. 30 through 43, to artificial neural networks. Thesediscussions are important because AI and ANN are also applied to IMSAs,to the negotiation process and to simulations, which will be addressedlater in the figures.

In his quest for software that would solve complex optimizationproblems, Holland sought a solution from nature. By emulating biologicalprocesses of breeding, mutation and survival of the fittest, he soughtto develop a new kind of software logic that would automatically improvein order to solve problems. His revolution in software design emerged asgenetic algorithms that are binary representations of genes that undergoevolutionary processes similar to biological entities. FIG. 26 describesa flow chart of a genetic algorithm. After a population is created(2610) (and mutations added to the population (2620)), each member ofthe population is evaluated for fitness (2630). The weak members arepruned out (2640) and removed (2650) while the strongest members areselected for crossover (2660), such as breeding, which is then performed(2670). A feedback loop is generated in order to generate multiplegenerations of a population or a range of sub-populations. Thesuccessful candidates are put into the population to replace the weakmembers (2680). In this way, the population of possible algorithmsevolves to an optimal solution. FIG. 27 shows an example of a binarygenetic algorithm crossover in which 2710 is bred with 2720 to achieve2730. In this example, a combination of “zero” and “one” yields a one,while two zeros or two ones combined in a specific position produces azero.

Holland's student, Koza, developed genetic programming based not onbinary algorithms but on the evolution of trees diagrams. FIG. 28 showsa genetic programming model with a crossover from the first phase of atree on the left with a tree on the right. In this example, thetriangular grouping on the upper left (in the box) (2830) is combined(2880) with the tree of the upper right (including the triangulargrouping in box (2970)), though the two groupings are “switched” rightto left in the examples. This tree structure modeling approach moreclosely resembles the actual genetic representation of evolutionaryprocesses.

The process of producing multiple generations of algorithms may take anenormous amount of time because there may be many thousands ofgenerations before a solution to a problem emerges. In order toabbreviate this process, the evaluation part of the process may beperformed in a parallel way. By breaking down the fitness evaluationfunction, the process is expedited. FIG. 29 uses the tree structuremodel to illustrate parallel subpopulation fitness evaluation in whichtwo main triangular structures (2910 and 2920) break into a large numberof smaller sub-populations (2930 and 2940) in order to assess thefitness of the best set of pairings. A final pairing is then selected(2950). Rather than running through a single sequence of the fitnessassessment procedure, the parallel approach is much more time sensitive.This time sensitivity is more conducive to adaptive systems in whichreal-time interaction is critical.

Genetic algorithms, genetic programming and evolutionary computationtechniques are applied to artificial neural networks in order to (1)calculate the initial weight and the connection weights of the signalbetween neurons, (2) train and optimize the connection weights, (3)generate the architecture and topology of a NN and (4) analyze thepattern, structure and phase state of a NN. GA, GP and EC are alsoapplicable to a range of complex computation problems, including (1)distributed problem solving, (2) group learning, (3) group cellularautomata simulations, (4) routing of computation resources in thedistributed system, (5) scheduling in a dynamic distributed system, (6)creating a self-organizing map, (7) solving optimization problems, (8)performing game theoretic simulations, (9) performing parallel datamining and (10) selecting a winner from among complex aggregationchoices.

FIGS. 30 through 43 deal with artificial neural networks. ANNs andevolutionary ANNs have numerous applications to the present system,particularly (1) organizing and optimizing distributed networks, (2)performing dynamic data mining, (3) organizing indeterministic learning,(4) ordering and adapting simulations, (5) modeling and optimizingdynamic game theoretic interactions (6) structuring adaptiveself-organization and (7) general problem solving. The field of neuralnetworks has evolved in the last generation from a purely theoreticalendeavor of logicians, mathematicians and neuro-biologists to includeapplications that are useful for practical systems. The present systemis an example of an application of complex neural networks to learning,simulation and adaptive processes. The neural networks are computationalrepresentations within the program code of MRA hardware that provideuseful tools for calculations of specific solutions to problems.

ANNs are parallel computational systems including interconnected nodes.Sometimes called connectionism because of an emphasis on the connectionsbetween the nodes, ANNs have inputs and outputs in the connectionweights between nodes. An ANN node represents an artificial neuron thatis modeled after biological neurons in a brain. A perceptron is thestructure that represents the sum of a neuron's inputs and outputs.

FIG. 30 shows a two layer neural network in which inputs are entered onthe left side and outputs are registered on the right side of thefigure. A feedback connection can be added that directs the connectionsback to the left side of the nodes. In FIG. 31, a multi-layer ANN isrepresented, with 3120 and 3140 representing the first layer, 3110, 3125and 3150 representing the second, hidden, layer, and 3130 representingthe output layer. In this example, the ANN structure is a multilayerperceptron (MLP). The connection weights are illustrated in numericalterms in this figure, with the bottom part having higher numbers thanthe upper part. There are a number of types of neural networks that maybe useful for various functions of a distributed mobile multiroboticsystem, including the MLP illustrated here, the Hopfield Network, theHebbian Network, the Boltzmann Network, the Bayesian Network, theevolutionary ANN (neuroevolution) and the recurrent Net. These types ofANNs can be classified as having feed-forward recall or feedback recall,being deterministic or indeterministic and, finally, possessingsupervised learning or unsupervised learning.

In FIG. 32, genetic programming is used to calculate initial connectionweights. The GP randomly generates a population, computes the fitness ofits members, generates a new population by performing a crossover of thefirst generation and adding random mutations and, finally, seeks toidentify the fitness of specific members of this most recent populationby comparing the best fit members with the criteria to satisfy theproblem of identifying the initial weight of the connection. FIG. 33shows how genetic programming is applied to an indeterministic ANN.

In phase one, the multi-layer ANN has inputs that register higherrelative numbers at the top (connections between 3315 and 3310, between3310 and 3320 and between 3325 and 3320) of the network than at thebottom (connections between 3315 and 3325, between 3325 and 3330 andbetween 3325 and 3320) of the network. As the network grows, shown inphase two, it emphasizes growth at the top, where there is significantlyhigher activity, and adds nodes at 3340 and 3350, while lower positionednodes at 3355, 3360 and 3365 become inactive. FIG. 34 shows theautomatic generation of a new node (3450) and a new connection (between3420 and 3450) through a mutating process. This process of mutation andgrowth through node (and connection) addition(s) provides anevolutionary model of ANN change called neuroevolution. GP calculatesboth the addition of the node, the addition of the connection and theconnection weights. In addition, GP can simultaneously calculate thenode/connection additions, connection weight changes and thearchitecture of the evolving ANN particularly in a distributed networkusing parallel computation techniques. FIG. 35 illustrates anevolutionary ANN indeterministic feed forward progression from the firstphase to the second phase. In the second phase, new nodes (3560 and3580) and their connections are added, while less active nodes (3550 and3570) and their connections are made inactive. In this way, ANNsconstantly “rewire” the network towards more productive nodes.

FIG. 36 shows a 3-2-1 multilayer network in which connection weights arecalculated by genetic algorithms. The algorithms are represented asbinary units in order to calculate the connection weights. The networkis trained by fine-tuning the connection weights through a process ofoptimization that the successive generations of genetic algorithmsperform.

Fuzzy logic is a method to provide new approaches to computing thatincludes terms like “maybe,” “possibly” and other partial and softdescriptions. Also called soft computing, FL represents a departure fromtraditional hard computing with mutually exclusive logic. FL usesstatistical methods to compute solutions to complex real world problems.FL is applied to ANN to produce complex adaptive networks. FIG. 37describes a FL module. A sensor provides crisp data input (3710) to afuzzifier module (3720), which is fed random mutations (3770) andproceeds with the fuzzification process. At this point, a fuzzy analysisproceeds in a fuzzy inference engine (3730) that operates according tofuzzy rules (3780) which are themselves adapted (3790). After the fuzzyanalysis process, the defuzzification of data occurs in the defuzzifiermodule (3740) where crisp data is output (3750) and presented toactuators (3760) for functional performance. This process is similar toa the process a signal undergoes in conversion from an analogue waveformto a digital mode by way of an analogue-digital (A to D) converter, or,contrarily, from a digital to an analogue signal by way of adigital-to-analogue converter (DAC).

FIG. 38 shows a neuro fuzzy controller with two input variables andthree rules. The input variables A1 (3810) and A2 (3820) provideconnections to the rule base R1 (3830), R2 (3840) and R3 (3850), whichthen provide an output at X (3860). FIG. 39 shows a five layer evolvingfuzzy neural network, with the input layer (3910), the fuzzificationlayer (3920), the rule node layer (3930), the decision layer (3940) andthe output layer (3950). A more complex ANN architecture is described inFIG. 40. In this figure, an adaptive network based fuzzy inferencesystem is shown in which inputs are presented to the initialpresentation layer, which is shown here in a parallel configuration,with R and S nodes. A training process occurs in the multilayer network(4040) that contains hidden layers. The outputs of this training processare fed to the consequent parameters (4050) that then lead to outputs.

A multilayer neural fuzzy inference network is illustrated in FIG. 41.The first layer (4110) generates the offspring (4115), which produceneural nodes at level three (4120) that are evaluated for fitness atlevel four (4125). The nodes breed a new generation with inactive nodes(4130) at level five. The surviving nodes (4135) again breed a newpopulation of nodes that result in two active members (4140) in layerseven. The successful mating of these nodes yields an output node (4145)at level eight. By breeding successive generations of successfulpopulations, and by training these successful populations, the networkis self-organizing and adaptive to its environment.

FIG. 42 shows a dynamic evolving fuzzy neural network. With five layers,including an input layer (4250), a fuzzy quantification layer (4255), anevolving rule nodes layer (4260), a weighted least square estimatorlayer (4265) and an output layer (4270). This model shows a complexsynthesis of simpler ANN representations.

One of the advantages of applying evolutionary computation to ANN isthat such advanced computing can be performed more efficiently by usingparallel approaches to break down a problem into smaller parts so that alarger number of computer processors may solve the problemsimultaneously. In this way, multiple MRAs may work on a problemtogether in order to accomplish the task in real time. One applicationof this approach is in the fitness evaluation part of the geneticalgorithm population production process. The problem of identifying thesuccessful candidates in a population can be performed, and expedited,by using parallel processing.

In another example of using parallel processing to acceleratecomputation problem solving, consider the problem of adding a neuralnode. By using parallel computation processes, not only can the neuralnode be added, but the connections to it can be added simultaneously; inaddition, the architecture of the network can be configured andreconfigured in real time, as new training models are considered andtested. The application of parallel algorithms to evolutionarycomputation, and, in turn, the application of EC (both GA and GP) to ANNprovide increasingly efficient approaches for use in a distributedmobile MRS. MRAs share ANN computation in a flexible way as illustratedin FIG. 43. Not only are the ANN not limited to the computation of asingle MRA, but MRAs (4310 and 4320) may share ANN computation resourcesbetween them. This distributed manifestation of parallel computationshows a flexible and extensible model in which the sharing of resourcesresults in increasingly efficient capabilities.

The application of EC and ANN to a distributed mobile MRS involvesseveral important areas, including learning, training, adaptation andprediction. In order for MRAs to interact with an uncertain, andchanging, environment, it must learn, predict and adapt. While EC isuseful to train ANNs, it is the general learning capabilities that areregarded as an outcome of this training process that ANNs ultimatelyprovide to MRAs and to the MRS that is critical to the effective realtime adaptation needed by the system. Many of the problems that a mobileMRS encounters involve evolving solutions, adaptive behavior patterns,complex predictive scenario modeling and self-organized processes. Theseproblems are solvable by applying EC and ANN models.

An example of the application of EC and ANN to an MRS is the modeling ofgame theoretic interactions. A particular strategy may be evolved for aparticular player based on a basic rule pattern selection organized by amultilayered feed forward perceptron. Each layer performs a calculationof the weights of inputs, connections and biases. A random number ofnodes is selected in the multilayer network, with a random number ofoffspring replicated from each parent and randomly mutated. A number ofrules of game moves are identified and consistently applied. Eachnetwork generation is evaluated for accurate effectiveness of achievinga successful game move. The network is trained and retrained with fullinformation. In this way, the learning process is refined so that eachplayer is able to optimally move according to the rules.

This straightforward application of EC and ANN to a game theoreticmodeling problem is relevant for a distributed mobile MRS because thepresent system uses simulations to model action. The simulations, whichare discussed below in FIGS. 68-73, can be either present-time-based ormay be based on future scenarios. Since in the case of the MRS, multipleMRAs provide sensor data inputs into the system and multiple MRAsprovide computation resources, the complexity of the game theoreticinteraction increases with the size of the network. Only EC and ANN,along with parallel computation of a mobile grid computing system, isable to calculate the increasingly complex problem solving algorithmsnecessary to organize such a model. The main systemic unit that is ableto organize such a complex architecture is the intelligent mobilesoftware agent (IMSA) operating within a multi agent system (MAS).IMSAs, introduced in FIG. 13, are discussed in FIGS. 44 through 49.

IMSA dynamics within the MAS are discussed in FIG. 44 in the context ofMRA interactions. MRA 1 (4410) launches a collaboration agent that isreceived by MRA 2 (4420) and collaboration between the two MRAs isinitiated. A search agent is launched from MRA 1 to search databases inMRA 3 (4430) and MRA 4 (4440). A negotiation between MRA 3 and MRA 4occurs by using intelligent negotiation agents (INAs). INAs are furtherdiscussed in FIGS. 48 and 50 through 58 below. Finally, analyticalagents are launched by MRA 4 to MRA 2 and MRA 3 in order to analyze aspecific problem. FIG. 45 shows IMSA relations between MRAs. MRAs areable to communicate with each other about complex tasks simultaneouslyby using IMSA specialist agent roles. There are number of specific typesof IMSAs, including analytical agents, search agents, collaborationagents and negotiation agents. FIGS. 46 to 48 briefly describeanalytical, search and negotiation agents.

In FIG. 46, analytical agents are described. After an MRA identifies aproblem (4610), it generates an analytical agent (4620). However, theprocess of initiating the AA begins with the generation of a searchagent, which is sent to multiple MRAs' databases with an initial query(4630). The search agent reports back to the initiating MRA with thepriorities of data in MRA databases (4640). The AA is then sent to theMRA in the order of priority sequence (4650) revealed by the search. TheAA analyzes the problem using specific methods (4660) detailed at 4670including MVA, regression analysis, pattern analysis, trend analysis andhybrid analyses. The AA develops solution options to the problem (4680)and shares the results with relevant MRAs (4690).

Search agents are described in FIG. 47. An MRA generates a search agent(4710) to query distributed MRA databases (4720). The search agentreceives initial feedback from databases regarding initial query (4730),refines the query with specific databases (4740), evolves searchparameters (4750) and seeks specific data sets among databases (4760).The search agent finds data sets as a result of the refined search(4770) and retrieves them for the MRA.

The general negotiation process is described with reference tointelligent negotiation agents (INAs) in a distributed network in FIG.48. The initiator INA meta-agent (4810) begins the process by launchinginitiator INA micro-agents to several other MRAs. INA micro-agent 1 islaunched to a negotiation session at INA 2's location (4820), INAmicro-agent 2 is launched to a negotiation session at INA 3's location(4825) and INA micro-agent 3 is launched to a negotiation session at INA4's location (4830). Each respective negotiation session occurs at eachINA's location within its MRA (2, 3 and 4, respectively). The initiatorINA interacts with INAs at the various remote MRA locations (or at itshome location) (4850), while a winner is determined at its home location(4855). Mutual agreement is reached, in this case between INA 3 and theinitiator INA (4860), while sessions are closed between the INA 2 andINA 4 negotiations (4865) and the overall negotiation process is closed(4870).

FIG. 49 describes an IMSA intercommunication with messenger sub-agents.Once an MRA makes a decision (4910), the content of the decision istranslated into specific instructions of action (4920) and the MRAcreates messenger sub-agents (4930). The MRA launches the messengersub-agents to other MRAs (4940), which then deliver the message with theinstructions to the MRAs (4950).

Because INAs are used in a critical way in a distributed mobilemulti-robotic system, they are further developed in FIGS. 50 through 58,including a description of the INA architecture, pre-negotiationprocess, INA logistics, negotiation process in a distributed network,multi-lateral negotiation process, multivariate negotiation factors,winner determination process, argumentation process and opposing INAstrategies.

INAs work by negotiating between at least two MRAs. INAs useargumentation methods to negotiate by presenting arguments with variableweights. INAs also negotiate about the best simulation to use in aspecific situation. In general, INAs use multi-lateral and multivariatenegotiation in order to come to agreement between noncooperating MRAs.In the case of competitive MRAs that negotiate for a compromise,problems are solved using group problem-solving and analyticaltechniques. Solutions to complex MRA group problems include the optimalor a temporary choice between solution options. Group problem solving isdiscussed in FIGS. 59 to 67. In all cases, AI is used in order tofacilitate the negotiation and problem solving processes.

In FIG. 50, the main INA architecture is described. Four INAs, includingan initiator INA (5020) and INA 2 (5010), INA 3 (5015) and INA 4 (5025)enter into a pre-negotiation session (5030), which is discussed morefully in FIG. 51 below. After pre-negotiation, all INAs negotiate in afirst session between the initiator INA and the several INAs (5040), butstops negotiating with INA 4 (5045). While the initiator INA continuesto negotiate with INA 2 and INA 3 in session two (5050), it eventuallystops the negotiation process with INA 2 (5070). However, the initiatorINA continues to negotiate with INA 3 in session 3 (5060) where itreaches agreement (5080) and closes the session (5090).

Referring to FIG. 51, the pre-negotiation process is described. After aninitiator INA requests negotiation terms (5110), an INA micro-agent islaunched (5120) and the initiator INA moves to other locations in orderto communicate with other INAs (5130). Several INAs, designated as S1(5140), S2 (5145 and Sn (5150) enter into a pre-negotiation process withthe initiator INA over parameters of the interaction session, includinglocation(s), protocols, rules and methods (5160). If they do not agreeon the negotiation parameters, they continue to interact until they doagree on these issues. The INAs agree on the rules of negotiations, thenumber of negotiation sessions and so on, based on the constraints(5170) and the initiator INA proceeds to the negotiation sessions withthe other INAs based on these pre-negotiation protocols, rules andmethods (5180).

In reference to FIG. 52, INA logistics are described. After initiatingthe session (5210), agents are generated and identified by codes (5215).The initial agent interaction protocols are generated (5220) in orderfor the agents to establish a common communication methodology. Suchcommunication processes involves translation (5225) and synchronization(5230). Failure to synchronize communication leads to a termination at5245. Once fully synchronized, INAs may construct unique negotiationstrategies using AI (5240) utilizing analytical agents (5235). At thispoint, agents signal the intention (5250) to negotiate with otheragents. After signaling to other agents, INAs send out communicationstreams (5255) to their home base, thereby constantly revealing to thehome base their locations, status and plans. At this point, theinitiator INA enters into a pre-negotiation session with the selectedINAs (5260) and launches micro-agents to negotiate with INAs atdifferent locations (5265). The INAs then enter into the negotiationprocess (5270) and either cease negotiation (5275) or come to anagreement (5280). If they cease negotiation, the INA settings are savedfor later (5285) and the session closed. On the other hand, if there isagreement, the MRA functions are activated consistent with the agreementreached (5290).

FIGS. 53A and 53B illustrate the negotiation process in a distributedsystem with mobility between INAs. The present example focuses on aone-to-one negotiation between an initiator INA and INA 2. After aninitiator INA initiates a negotiation session with INA 2 (5310), theINAs identify possible locations (5315) and specify agreed locations(5320) at which to negotiate. In the illustrated example, the initiatorINA moves to INA 2's location (5323) with program code. INA 2 identifiesincoming initiator-INA entry after activation and security protocolapproval (5326) at INA 2's location.

The agents engage in (5330) and complete (5333) negotiation tasks, afterwhich the initiator INA notifies its home MRA of its remote locationactivities by sending a message (5336). After reviewing more tasks atthe remote INA 2 location (5340), the initiator INA either terminates(or returns home) (5343) or assesses additional tasks using internaldatabase and analysis (5347), assessment (5350) and identification(5353) of the next location for task execution and moves to anotherlocation (5356).

After moving its program code (5360), the initiator INA identifies aneed for AI computation (5363), requests AI computation resources at aspecified location (5367), identifies available AI computation resources(5370) and messages a request for AI computation resources to be sent toa specific location (5373). The initiator INA receives (5377) and teststhe AI computation resources at a specific negotiation site (5380). Thenegotiations are completed at the remote location (5385) and theinitiator INA returns home (5390).

As shown in this figure, though a one-to-one interactive negotiation isshown between an initiator INA and another INA, an initiator INA (or itsmicro-agents) may negotiate simultaneously with at least two INAs at twoor more INA locations in another embodiment.

FIG. 54 shows a simultaneous multi-lateral negotiation process withmultiple variables. At each phase in the process, denoted on the leftcolumn, INA 1 is in the position of negotiating with six INAs, listedhere as 2 through 7. In the first phase, after negotiation with INA 2 inthe first session, INA 1 negotiates with INA 3 in the second session. Inthe third session, INA 1 negotiates simultaneously with INA 2 and INA 3on the second phase of negotiation with each. In session four, however,INA 1 begins to negotiate with INA 4, while it continues to negotiatewith INA 2 in a third phase. Similarly, in the fifth session, INA 1continues to negotiate with INA 4 in a second phase, while it begins tonegotiate with INA 5 in a first phase. The sixth session continues thisapproach of continuing with INA 5 in a second phase while it initiates anegotiation with INA 6, and so on in session seven.

FIG. 55 shows multivariate negotiation factors in which, in the firstphase, MRA 1 negotiates over specific variables with MRA 2, rejectingsuccessive possible variables until finally agreeing on, and thusselecting, “Z”. In the second phase, MRA 2 negotiates over specificvariables with MRA 3 in a similar way, also resulting in the agreementover, and selection of, “Z”. This process of negotiating over a numberof factors shows the key element of “convergence” to negotiation. Byrepeating this process a number of times, many INAs may agree with eachother about numerous factors in a complex dynamic system.

FIG. 56 shows the tournament style winner determination process in acompetitive INA framework. Several INAs (2 through 5) enter into anegotiation with an initiator INA (5650) in phase one. The initiator INAagrees to narrow down the field to INA 2 (5660) and INA 4 (5670) inphase two. Between these finalists, the initiator INA then selects thewinner, INA 4 (5690) in the third phase.

The argumentation process is shown in FIG. 57. During consecutivetemporal phases of a negotiation process between MRA A and MRA B,several key factors are isolated and accepted by each MRA. First,negotiation variables are accepted by MRA B. Second, MRA A prunes outvariables that it will not compromise on. Next, MRA B prunes outnon-negotiable variables. Finally both MRAs determine the key variablesthat the will compromise on.

Negotiation is a process that fits into the overall game theoretic modelthat organizes competitive agents across limited goods. In this sense,negotiation involves agent strategies that anticipate opposing agentstrategies. FIG. 58 shows the anticipation of opposing INA strategies.After INA1 presents an argument to INA2 (5810), INA 2 evaluates theargument (5830) using multi-variate analysis and regression analysis(5820). INA2 anticipates INA1's strategy by examining the trajectory ofarguments (5840), which it performs by identifying cues to anticipatebehavior in its environment (5850). INA 2 then presents acounter-argument to INA 1 (5860). INA 1 anticipates INA 2's strategy byanticipating its possible argument scenarios (5870) and the INAseventually reach an agreement (5880).

FIGS. 59 through 67 describe group problem solving.

In FIG. 59, problems are identified by MRAs and the collective agrees tonarrow the focus of the problem. Any MRA in the group can identify aproblem (5910), in sequence, such as “How to carry out a mission withother MRAs?” (5920), “How to combine with other MRAs for a commonmission?” (5930), “How to target an object with a group of MRAs?” (5940)or other mission or goal based problems (5950). The group of MRAsprioritize problems by assigning values to each problem and orderingthem by rank in real time (5960) so that potential solutions can be madein the ranking order (5970).

Solution options between MRAs are described in FIG. 60. A sharedfour-dimensional grid is created by MRAs in order to represent theframework of a potential field (6010). Simulation scenarios from the MRAgroup are tested in order to detect the best fitting solution for aspecific option (6040) after analyses are performed on specific solutionoptions by MRAs (6030). A competition is then established betweenvarious potential solutions for the best solution available (6050) andweights are attached to each solution option (6060) which allows thesimulation scenario solution options to be ranked (6070).

FIG. 61 describes the solution option selection method developed andapplied by MRAs. An MRA develops a benchmark of methods in order toselect a simulation scenario (6110) and then applies an experimentationprocess to test possible solutions (6120). The shortest path option isselected as a default without environmental interaction (6125). But theMRAs interact with the environment (6160), a process that is informed byactual environmental change (6150). The MRAs receive the results of theenvironmental interaction (6170) and evaluate the results (6180). EachMRA has a distinct vantage and thus applies a unique analysis (6190).The MRAs prioritize the results by weighting them for probability ofsuccess and by ranking them in the order of highest probability (6130).The methods of solution selection are refined (6135) and a feedback loopis structured to apply continued experimentation, when combined withcontinued environmental interaction, in order to continue to refine themethods of solution selection. A winner is selected from the possiblesolution options (6140) and the optimal solution is selected for apossible scenario (6145).

There are times when an optimal solution to a problem is not possible.In these instances, the best we can hope to achieve is the bestavailable solution in a specific circumstance. FIG. 62 describes thisprocess of selecting the best available, not the optimum, solution, to aproblem, while waiting for the most recent relevant information. TheMRAs work together to establish a list of solution options (6210), whichare filtered according to constraints (6220) by time, optimization,combinatorial optimization, accuracy, quality of information and bypruning out what is not probable (6230). The MRAs then apply solutionoption methods (6240) which are refined by interaction with theenvironment (6250). The MRAs either (1) undergo a convergence ofagreement (6250), in which case they select a specific simulationscenario solution option (6275) and carry out a mission (6285), (2)partially agree with an overlap of interests within constraints (6260)and (3) temporarily agree (within constraints) (6265), in which casesthey select merely the best available simulation scenario solutionoption (6280) and carry out a mission within these constraints (6290).On the other hand, the MRAs, may not agree at all (6270), in which eventthey must return back to the earlier phases of the process of filteringthe solution options (at 6220). FIG. 63 shows an illustration of MRAgroup agreement.

In FIG. 63, part (A), three MRAs present arguments that are representedas small circles within the larger circles. The gray area that shows theoverlap of the three MRAs signifies the common interest between thethree. In the second diagram at (B), the best available optimum scenariois shown in the gray area with time constraints. The configuration ofthis optimum window of opportunity, because it is time constrained,changes with the changing circumstances of the environment.

Clearly, the time aspect of the decision process is important becauseperfect information is rarely available and because agents in amulti-robotic system that interact in uncertain and dynamic environmentsbenefit from waiting for the latest available information beforedeciding to act. FIG. 64 shows the temporal aspect of the decisionprocess, with the left column representing the temporal component, thesecond column representing the physical state of the multi-roboticsystem and the right column representing the analytical state of themulti-agent system. In the first line, past physical experiencesinfluence past data flows, while past data flows affect futurescenarios. Future scenarios affect present analysis and decision-making,which influence the selection of a preferred scenario of action. Thissection of the preferred scenario influences the present course ofaction. In this way, the analytical and physical states of the systemhave causal connections over time. These interconnections reveal theintegration between the MAS and MRS.

The group problem solving process requires specific analytical methods,including multivariate analysis, regression analysis, trend analysis andpattern analysis, in order to select a successful candidate. FIGS. 65,66 and 67 describe these analytical tools.

In FIG. 65, multivariate analysis is applied to problem solving. Aproblem is forwarded to an MVA filter (6510), which strips the variablesfrom the problem and analyzes each variable in isolation (6520). The MVAfiltering process forwards the variable analysis procedure to multipleMRAs (6530) using parallel processing, where each MRA analyzes variablesand compares this analysis with other MRA analyses (6540). The MRAs rankthe multiple variables and share with the results between the MRAs(6550). The variables are evaluated in each solution option (6560) andthe best available solution is selected from solution options (6570).

In FIG. 66, regression analysis is applied to the problem solving ofconflicting MRAs for a winner determination. The MRAs analyze a problemwith a regression analysis filter (6610), sort through various variables(6620) and share the data between them (6630). Again, the MRAs dividethe analysis between them in order to benefit from the advantages ofparallel computation. The MRAs weight the variables by establishingpriorities and comparing each variable with program parameters (6645).The MRAs evaluate the importance of the variables by comparing them withdata sets in the distributed database (6650) and then rank thepriorities of variables (6660) and apply the ranking of the problemvariables to solution options (6670). MRAs select the best solutionoption by applying the program parameters (6680).

In FIG. 67, pattern analysis and trend analysis are applied to problemsolving of conflicting MRAs for winner determination. Depending on whichtype of analysis is required, a problem is formulated (6710) and eitherpattern analysis (6720 and 6730)) or trend analysis (6725 and 6735) isapplied. The pattern analysis approach analyses regularities in spatialcoordinates using statistical methods (6740), while trend analysisanalyses regularities in temporal coordinates using statistical methods(6745). In either case, each analysis is evaluated (6750), the resultsranked (6760) and the analyses are applied to MRA decision logic (6770).The MRA group then makes a decision based on these analyses andformulates a plan (6780) that the group is able to activate (6790).

Much of the substance of the problem solving, and negotiation, processesunderlying inter-MRA conflict involves simulations. Because MRAs aremechanical entities that assume physical shape and mobility in space andtime, it is possible to model them by using simulations. The MRS may usea number of types of simulations, including cellular automatasimulations, particle simulations and game theoretic simulations. Allthree main types of simulation add valuable qualities to therepresentation of complex activities in a mobile distributedmulti-robotic system, including structuring the dynamics of aggregationprocesses. FIGS. 68 through 73 describe the cellular automata simulationof MRA group activities.

Cellular automata (CA) is a system of cells which are representeddigitally as a binary unit or vacuum. As objects move through a grid,they fill up the space in the cell. If an object does not occupy a cell,it is empty. In this straightforward way, CA can simulate groups ofobjects in space and time. CA's may include two dimensional,three-dimensional or four-dimensional (i.e., including the timedimension) structures. Once including the time dimension, it is possibleto model CA simulations. CA simulations are well suited to representmobile distributed multi-robotic systems because the MRAs are seen asmerely objects that move in space and time across a map in anonoverlapping environment. Though the simulations may be complex, forinstance, in modeling dynamic coalitions in adaptive sequences as theyinteract with a fast changing environment, their representation iscritical in order to provide a mechanism for the self-organization ofthe MRS processes.

FIG. 68 shows the modeling of MRS activity with simulations in asituation assessment. As the illustration shows, a cubic space isoccupied by mobile agents, represented here as A, B and C. In the caseof this situation assessment, the map describes the change in spatialposition of the agents from A1 to A2 to A3 (6840), from B1 to B2 to B3(6850) and from C1 to C2 to C3 (6860).

FIG. 69 describes synchronizing simulations within an MRA cluster. AnMRA sensor detects other MRA locations (6910) and converts the analoguesensor data to a digital form (6920). The MRA data about other MRApositions is analyzed in real time to show phase state changes (6930)and a simulation is constructed to represent data about MRA positionchanges (6940). Each MRA continuously tracks all MRAs in the system inreal time (6950) by using this approach and each MRA constructs asimulation to represent MRAs in the system (6960).

FIG. 70 describes a CA scenario option simulation. Two scenario optionsare presented for A and B. For scenario option A, MRA 1 (7010) and MRA 2(7020) move across four phases to objects X and Y. For scenario optionB, MRA 1 (7030) and MRA 2 (7040) move across the four phases towardsobjects X and Y but in a different path.

FIG. 71 describes a reversible, or deterministic, CA in which asimulation is constructed by projecting backwards from a goal. Thoughthe scenario option representations look very similar to FIG. 70, thephasal process that is used is exactly opposite the causal approach.Rather, in this simulation model, the MRAs begin with the goal andproject backwards. By using this reversible approach, the CA simulationis presented with a more goal-oriented solution.

FIG. 72 shows how adaptive geometric set theory is applied to an MRS.The three CA models of A (7210), B (7220) and C (7230) show threedifferent sequences from one to three reflecting different positions. Inthe converged model (7240), a combination of the three models is reachedwhich synthesizes the three by compromising the outcomes of B and C.Geometric set theory is useful to represent the overlap of aggregatedsets.

FIG. 73 shows the selection of an optimal simulation as a (temporary)convergence of simulation scenarios. MRA 1 is represented by actualpositions at 1′, 1″, 1′″ and 1″″ (7320) while a possible scenario isrepresented by 1R″, 1R′″ and 1R′″ (7310). Similarly, for MRA 2 (7330)and the possible simulation scenario (7340). Finally, the outcome forthese sequences is a convergence of MRA 1 at 7325 and of MRA 2 at 7335.

FIGS. 74 through 78 describe the aggregation process in a multi roboticsystem. FIGS. 75 through 84 describe the dynamic coalition (orreaggregation) process in a MRA and FIGS. 85 through 88 show autonomousMRS self-organizing processes.

FIG. 74 describes the aggregation initiation process in which sets ofMRAs form from a larger collective. The MRAs develop and presentsimulations (7410), test the simulations (7415), prune out the leastuseful simulations (7420), and compare the best simulations with theenvironment (7425) and with (updated) program parameters (7430). Thebest simulations (within constraints) are selected (7435) and converged(7450) in order to create overlap. From the converged simulations, a mapis created (7455) and individual MRA locations are identified relativeto their positions on the map (7460). The MRAs then move their physicallocations in an efficient way according to the geometric location of theconverged simulation map (7465).

The initiation of homogenous MRA group formation is described in FIG.75. In the first (top) section, an object X (7510) is confronted withseven similar MRAs (7520). After undergoing an aggregation initiationphase, the MRAs (7540) are shown in the second section as changing theirposition with regard to object X (7530) by moving towards the object.

In FIG. 76, the initiating process is shown involving commonheterogeneous MRAs. In the first phase, an MRA with type “S” (7610)initiates a group of specialized MRAs (7620). In the second phase, the“S” MRAs (7630) concentrate in order to perform a specific task, whilethe other types of MRAs (7640) retain their positions. In this case, aparticular type of specialized MRA is “picked out” in order to perform aspecific function as a specialized unit.

In FIG. 77, a complementary heterogeneous MRS group formation initiationis described. In the first phase, the MRA with type “S” (7710) initiatesa group of specialized MRAs in a similar was as with commonheterogeneous MRAs. However, rather than attracting the same “S” type,it requests the “Y” and “T” types (7730) from the second column whichleaves the other MRAs in their stable positions (7740). In this way,complementary specialists may work together as a team to perform complexfunctions in tandem.

The first phase of a demand-initiated environmental adaptation isdescribed in FIG. 78. From the combination of static environmental datamaps (7830) and actual environmental changes (7820), dynamic environmentdata maps (7825) are created. These maps inform past and presentsimulations (7850), which are analyzed (7840). The analysis is itselfinformed by learning methods (7810). Given the simulations and theiranalysis, negotiations occur between the MRAs (7855), which reach adecision, within limits (7865). This decision is also informed bylimited (converged) scenario simulations (7870). Once a decision is madeby MRAs, the selection is made about the specific form of aggregation touse (7875) and the actual special positions of the MRAs are changed inaccordance with this new decision (7880).

In FIGS. 79 to 84, dynamic coalitions, or re-aggregation processes, arediscussed. In FIG. 79, the continuous MRA group compositionreconfiguration process is described. In the first phase, a group isconcentrated (7910) that includes MRAs 1, 2, 3 and 4. In the secondphase, a new grouping is organized (7930) that includes MRAs 3, 4, 5 and6. Finally, in the final phase, yet another grouping is organized toinclude MRAs 4, 5, 6, 7 and 8. The movement through the system from theleft part of the group to the right part of the group illustrates thechanging interaction response to the environment that requires thegrouping to adapt to different sub-sets of the larger collective.

In FIG. 80, the continuous reconfiguration of sub-networks is described.In this figure, the right column shows an object that the MRA group(s)on the left move towards. The first phase of the process is identifiedin the right column. In the first part of the process, at 8005, thefirst sub-set of the collective moves towards the object. In a secondphase, the MRAs reconstitute the configuration of the MRA grouping(8010) and move toward the object. In a later phase, in the middle map,a larger initial grouping, including six MRAs (8015) move toward theobject, while a second grouping (8020) moves to the object later. Thissecond group includes the overlapping two members of both groups.However, in the third part of the process, the demand for MRAs changesagain from the second part of the process. In this case, five MRAs movetoward the object, while a grouping including six MRAs (including thelast three of the first phase) move towards the object. This figuresillustrates the dynamic motion aspect of the aggregation process ascoalitions are dynamically created and reconfigured.

FIG. 81 illustrates dynamic group behavior adaptation to environmentalinteraction. In the first phase, the first MRA grouping (8120) movestowards a group of objects (8130). In this first phase, one object isknocked out, represented by an X, but two MRAs are also removed. In thesecond phase, the reconstituted group of MRAs (8150), which includes thecombination of 8120 and 8110, move towards two more objects in the groupof objects (8160) and three MRAs are removed from action, as representedby an X's. In the third phase, the newly reconstituted MRA group (8170)that includes a combination of 8150 and 8140, move towards the threeremaining objects (8180).

The parallel dynamic traveling salesman problem is described withcooperating autonomous agents in FIG. 82. After they receive a sensordata stream (8210), a group of MRAs collect environmental data bysharing sensor data (8240) and use the initial prioritization ofenvironmental data consistent with program parameters (8250). As theenvironmental data changes (8270), an interaction between MRAs and theenvironment occurs (8820) which informs the MRA sensor data stream(8210). The environmental data changes (8270) also reprioritize theorder of priorities with the latest information of a changingenvironment (8280); this reprioritization of the order of priorities arelargely based on the MRAs' prioritization of a physical sequence (8260)based on a reprioritization of MRA program parameters (8230). Once thereprioritization of priorities with the latest information (toaccommodate a changing environment) occurs, the MRAs perform a physicalsequence of actions in the order of priority (8290). This processinvolves a dynamic connection between the analytical functions of theMAS and the physical processes of sensor data gathering from multiplechanging MRA positions that yields variable data inputs from a changingenvironment. Because the MRS is distributed, the use of parallelprocessing allows increasingly efficient processing of computationresources. FIG. 64 also illustrates a data flow process thataccommodates both physical state and analytical state dynamics acrosstime.

FIG. 83 shows altruistic MRAs sacrificing themselves in order to acquiresensor information to increase the chances of overall mission success.The MRAs shown with an X, move toward the object (8320) and are knockedout. However, the information that is obtained in this gambit mission isthen sent back to the collective so that they are better able to defeatthe object.

The general dynamic coalition process is described in FIG. 84. Aftermission goals and parameters are established (8455), sensor data andvarious sources examine the terrain (8460). The simultaneous parallelcomputation by numerous agents is performed by sharing data and bydividing computation resources (8465). The sensor data is then evaluatedby various MRAs (8470). Groups of MRAs begin to emerge by agreeing toaggregate (8475). Decisions are made to form smaller groups in order tomeet evolving mission parameters and priorities. Specified MRAs updatethe navigation plans and activate the mission (8485). As the missionevolves, groups of MRAs are added or removed as needed, for instance ifthe opposition is particularly hostile (8490).

FIG. 85 describes the group coordination and obstacle avoidance processthat is involved in autonomous MRS self-organizing processes. ObstaclesX, Y and Z (8510) move towards MRAs A, B and C (8550) from their initialpositions. As the objects get closer, at 8520, the MRAs detect theobjects as obstacles, at 8540, and begin to avoid them by moving out ofthe trajectory of the moving objects (8530).

In FIG. 86, specific MRAs A (8610), B (8630) and C (8650) move towardsspecific objects X (862), Y (8640) and Z (8660), with A attacking Z, Battacking X and C attacking Y. This specialization of a self-organizingprocess is further developed in FIG. 87 as a specialized group of MRAswork together as a team. MRAs A (8710), B (8720), C (8780) and D (8790)move into positions 8730, 8740, 8760 and 8770, respectively, in a phasein the process towards assembling together at 8750. In this position,the specialized MRAs work together sharing specific functions forgreater usefulness on a mission. In FIG. 88, multi-functional MRAs aredescribed in a self-organizing process. Whereas in FIG. 87, the MRAs arespecialized, in FIG. 88, the MRAs have multiple functions that mayswitch in specific changing circumstances. As the figure shows, MRA A inposition A1 (8810) and MRA B in position B1 (8850) move towards object X(8830). As they move towards the object, the MRAs detect the need tochange from one specialized function to another. At positions A2 (8820)and B2 (8840), the MRAs change their functional mode to a differentspecialty in order to be more effective in their mission against theobject.

FIGS. 89 through 99 describe specific applications of the presentsystem. There are three main categories of application, including (1)remote sensing (described in FIGS. 89 to 92), (2) hazard management(described in FIGS. 93 to 95) and (3) building processes (described inFIGS. 96 to 99). Remote sensing activities that use an MRS includesurveillance, reconnaissance, remote exploration, sentry activities andcinematography. Hazard management activities include toxic siteclean-up, oil spill and fire fighting activities. Building processesinclude manufacturing production and assembly, road building andsurgical activities.

In FIG. 89, surveillance and reconnaissance is described using multiplemicro objects for sensing and tracking of a mobile object. As two MRAs,X (8910) and Y (8960) move in parallel tracks to positions X2 (8920) andY2 (8970), respectively, they track object A (8940). As the object movesto position A2 (8950) and then to position A3 (8955), MRA X moves toposition X3 (8930) and then to position X4 (8935), while MRA Y moves toposition Y3 (8980) and then to position Y4 (8990) by using sensors andby tracking the object closely.

In FIG. 90, a remote exploration process is described in which theinitial tracking of multiple objects is performed by multiplemicro-MRAs. In this example, MRA1 (9010) moves towards object R1 toposition X′. However, the object itself moves, from position R′ toposition R″ and is followed by the MRA, which moves to position X″. Thisprocess is repeated with MRA 2 (9020) tracking object R2 (9060) and withMRA 3 (9030) tracking object R3 (9070).

FIG. 91 describes sentry activity within limited perimeters defendingmultiple objects with a multiple number of MRAs. In this illustration,the MRAs are spaced evenly apart in order to occupy a constrained fieldaround the perimeters of a field.

The current system is also applicable to cinematography, wherein onemobile object (or cluster of mobile objects) are sensed and tracked withMRAs. This process is described in FIG. 92. MRA 1 (9210) and MRA 2(9270) track object X (9240) as it moves to positions 9250 and 9260. MRA1 tracks the object along a path to position 9220 and 9230, while MRA 2tracks the object along a path to position 9280 and 9290. This processmay be variable so that as the object stops to pause, the MRAs stop aswell. In this case, the MRAs have automated digital photographiccapabilities with on-board auto-focus zoom lenses and data storage. TheMRAs can be used to track multiple objects as well. One MRA may trackthe object(s) in a close in view while the other MRA(s) may track theobject(s) from a distance in order to obtain a different view of thesame scene.

FIG. 93 describes a toxic site cleanup. In this case, a static cleanupoccurs within land perimeters by multiple MRAs. In the first phase,A-type MRAs (9310) are used to confine a limited amount of toxiccontamination (9320) in a specific physical space. The MRAs move byusing a side-to-side sweeping approach. In the second phase, the spill(9340) has been reduced and the MRS calls in the B-type MRAs (9330) inorder to continue to eliminate the contamination by using a similarsweeping technique. Finally, as the toxic spill (9620) is controlled ina finite space, the MRS calls in the C-type MRAs (9350) to complete themop up operation.

In a similar way as cleaning up toxic spills on land, FIG. 94 describesa dynamic clean up of an oil spill within limited hydro perimeters bymultiple MRAs. In the first phase, the oil spill (9420) is surrounded byMRAs (9410), which operate to limit the damage and remove the oil. Inthe second phase, the oil spill is rendered smaller (9440) and MRAs(9430) continue to operate to remove the oil by operating in specific“cells” that act to sweep up the spill. This process continues in thefinal phase in which the oil spill (9470) is confined and the finaldrops of oil are mopped up by the MRAs (9460).

FIG. 95 describes the automated fire fighting process in which dynamicinteraction occurs with a complex environment by multiple MRAs. In thefirst phase of the process, MRAs (9510) are dropped to the fire (9520)on one facade only (because the fire is initially inaccessible on theother side). As the MRAs (9530) are able to surround the fire (9540), inthe second phase, they seek to put it out by using several methods,including removing brush that is flammable, by pouring fire retardant ina line around the fire and by directly pouring water on the fire. TheMRAs may be air launched or ground launched and retrieved. In the finalphase, the fire is reduced (9560) and the MRAs (9550) complete the taskof extinguishing the fire.

FIG. 96 describes the manufacturing production process in which anobject is created by using multiple MRAs. MRAs A (9610), B (9640), C(9650) and D (9630) work together to create the object (9620). One wayto do this is for each MRA to attach parts of the object together fromdifferent spatial positions.

FIG. 97 shows the assembly of an object by using MRAs to combine theparts. At an assembly facility (9710), MRA A at position A1(9720) andMRA B at position B1 (9730) act to assemble objects. Rather than havinga movable assembly line, in this case, the MRAs themselves move. MRA Amoves to position A2 (9740) and MRA B moves to position B2 (9750) inorder to complete the assembly task. This process of organization ofassembly tasks provides the opportunity for specialized functional MRAsto work together as a team in order to assemble objects by combiningparts more quickly.

Roads can be built by using multiple MRAs as illustrated in FIG. 98. MRAA (9810) and MRA B (9820) proceed to create a road by laying downasphalt along adjacent tracks.

FIG. 99 describes micro surgery using MRAs for trauma intervention andstabilization. In this case, MRA A and MRA B guide themselves to thepatient. Initially, the MRAs ascertain, by using sensors, the symptomsof trauma in order to identify problems. The MRAs then move to variouspositions on the patient in order to solve the problems. In the case ofa wound, the MRA will seek to stop the bleeding by cauterizing the woundwith a laser or by applying pressure. In the case of heart stoppage, theMRA will administer an electric shock. By stabilizing a patient, thechances of recovery are dramatically higher.

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims. All publications, patents, and patentapplications cited herein are hereby incorporated by reference for allpurposes in their entirety.

1. A system comprising: a plurality of mobile robotic agents (MRAs); anda plurality of intelligent mobile software agents (IMSAs), the IMSAscomprising software means for allowing MRAs to communicate, interact andcollaborate with other MRAs; the MRAs being arranged to construct aself-organizing map of an uncertain environment by creating an initialmap based on initial sensor data organization, to moving to newlocations, obtaining sensor data from the environment at the newlocations; and filling out the initial map to create a fuller picture ofterrain to include formerly missing parts of the initial map.
 2. Thesystem of claim 1, wherein the MRAs are arranged to fill out the initialmap using caching techniques that add the most recent information to amap outline.
 3. A system for managing aggregated regrouping, comprising:a plurality of mobile robotic agents (MRAs); and a plurality ofintelligent negotiation agents (INAs); wherein the plurality ofsub-groups of MRAs are assigned their respective priorities; whereineach sub-group of MRAs is initially organized into a specific spatialconfiguration; wherein new sensor data on environmental change isorganized by INAs; wherein INAs determine the priority of MRA functions;wherein INAs transfer software code to MRAs; wherein MRAs are operableto receive code from INAs; wherein the specific spatial configuration ofeach sub-group of MRAs is determined based on information from one ormore inputs from the environment; wherein the specific spatialconfiguration of each sub-group of MRAs is changed in response to theinformation from one or more inputs from the environment.
 4. The systemof claim 3 where in a sub-group of MRAs continually adjusts itsconfiguration.
 5. The system of claim 4 wherein adjusting theconfiguration includes adding additional MRAs.
 6. A system for renderingdecisions for mobile robotic agents, comprising: a plurality of mobilerobotic agents (MRAs); and a plurality of intelligent negotiation agents(INAs); wherein initial program parameters are transmitted to MRAs;wherein INAs are used to transmit data between MRAs; wherein sensor datafrom member MRAs is transmitted to other member MRAs; wherein sensordata is weighted by INAs and ranked by priority of importance; whereinsensor data is further interpreted by INAs by comparing it with missionparameters; wherein the INAs calculate a plurality of possiblesimulations to meet mission goals; wherein INAs use a plurality ofmethods to test the plurality of possible simulations using the sensordata and the initial mission parameters to determine the best simulationto meet the mission goals; and wherein the INAs generate instructionsand transmit the instructions to the member MRAs to allow the memberMRAs to form an optimal geometric configuration according to the bestsimulation.
 7. The system of claim 6 wherein a plurality of INAsresolves the plurality of methods used to resolve a conflict and make adecision.
 8. The system of claim 7 wherein a plurality of INAs resolvesthe conflict by comparing MRA priorities with initial programparameters.
 9. The system of claim 7 wherein at least two MRAs providesensor data to allow a plurality of INAs to resolve the conflict.